Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Development and experimental validation of a finite element model of total ankle replacement.

Total ankle replacement remains a less satisfactory solution compared to other joint replacements. The goal of this study was to develop and validate a finite element model of total ankle replacement, for future testing of hypotheses related to clinical issues. To validate the finite element model, an experimental setup was specifically developed and applied on 8 cadaveric tibias. A non-cemented press fit tibial component of a mobile bearing prosthesis was inserted into the tibias. Two extreme anterior and posterior positions of the mobile bearing insert were considered, as well as a centered one. An axial force of 2kN was applied for each insert position. Strains were measured on the bone surface using digital image correlation. Tibias were CT scanned before implantation, after implantation, and after mechanical tests and removal of the prosthesis. The finite element model replicated the experimental setup. The first CT was used to build the geometry and evaluate the mechanical properties of the tibias. The second CT was used to set the implant position. The third CT was used to assess the bone-implant interface conditions. The coefficient of determination (R-squared) between the measured and predicted strains was 0.91. Predicted bone strains were maximal around the implant keel, especially at the anterior and posterior ends. The finite element model presented here is validated for future tests using more physiological loading conditions.

Leukemic cluster growth in culture is an independent risk factor for acute myeloid leukemia and short survival in patients with myelodysplastic syndrome.

In patients with myelodysplastic syndrome (MDS) precursor cell cultures (colony-forming unit cells, CFU-C) can provide an insight into the growth potential of malignant myeloid cells. In a retrospective single-center study of 73 untreated MDS patients we assessed whether CFU-C growth patterns were of prognostic value in addition to established criteria. Abnormalities were classified as qualitative (i.e. leukemic cluster growth) or quantitative (i.e. strongly reduced/absent growth). Thirty-nine patients (53%) showed leukemic growth, 26 patients (36%) had strongly reduced/absent colony growth, and 12 patients showed both. In a univariate analysis the presence of leukemic growth was associated with strongly reduced survival (at 10 years 4 vs. 34%, p = 0.004), and a high incidence of transformation to AML (76 vs. 32%, p = 0.01). Multivariate analysis identified leukemic growth as a strong and independent predictor of early death (relative risk 2.12, p = 0.03) and transformation to AML (relative risk 2.63, p = 0.04). Quantitative abnormalities had no significant impact on the disease course. CFU-C assays have a significant predictive value in addition to established prognostic factors in MDS. Leukemic growth identifies a subpopulation of MDS patients with poor prognosis.

Validation of the instability shoulder index score in a multicenter reliability study in 114 consecutive cases.

BACKGROUND: Anterior shoulder stabilization surgery with the arthroscopic Bankart procedure can have a high recurrence rate in certain patients. Identifying these patients to modify outcomes has become a focal point of research. PURPOSE: The Instability Shoulder Index Score (ISIS) was developed to predict the success of arthroscopic Bankart repair. Scores range from 0 to 10, with higher scores predicting a higher risk of recurrence after stabilization. The interobserver reliability of the score is not known. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: This is a prospective multicenter (North America and Europe) study of patients suffering from shoulder instability and waiting for stabilization surgery. Five pairs of independent evaluators were asked to score patient instability severity with the ISIS. Patients also completed functional scores (Western Ontario Shoulder Instability Index [WOSI], Disabilities of the Arm, Shoulder and Hand-short version [QuickDASH], and Walch-Duplay test). Data on age, sex, number of dislocations, and type of surgery were collected. The test-retest method and intraclass correlation coefficient (ICC: >0.75 = good, >0.85 = very good, and >0.9 = excellent) were used for analysis. RESULTS: A total of 114 patients with anterior shoulder instability were included, of whom 89 (78%) were men. The mean age was 28 years. The ISIS was very reliable, with an ICC of 0.933. The mean number of dislocations per patient was higher in patients who had an ISIS of ≥6 (25 vs 14; P = .05). Patients who underwent more complex arthroscopic procedures such as Hill-Sachs remplissage or open Latarjet had higher preoperative ISIS outcomes, with a mean score of 4.8 versus 3.4, respectively (P = .002). There was no correlation between the ISIS and the quality-of-life questionnaires, with Pearson correlations all >0.05 (WOSI = 0.39; QuickDASH = 0.97; Walch-Duplay = 0.08). CONCLUSION: Our results show that the ISIS is reliable when used in a multicenter study with anterior traumatic instability populations. There was no correlation between the ISIS and the quality-of-life questionnaires, but surgical decisions reflected its increased use.

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.