Digital optimization of teeth setup in an edentulous patient with partial glossectomy: A case report.

The purpose of this clinical report was to describe the use of a piezographic impression associated with computer-aided design and computer-aided manufacturing (CAD-CAM) for teeth setup and of digital tools for neuro-musculo-kinetic analyses. An edentulous patient with hemiglossectomy and heavily resorbed mandible consulted for complete denture rehabilitation to improve their masticatory function and speech. Master casts, wax rims, and piezographic impression were scanned for digital prosthetic work. Two digital try-ins were performed to respect the neutral zone: try-in 1 with posterior crossbite and try-in 2 without crossbite. Muscle activity and mandibular kinetics were performed for each try-in following the MAC2 protocol (six criteria): muscular tone, contraction synchrony, contraction efficiency, interocclusal rest distance, amplitude of mandibular movement, and velocity. Try-in 2 showed better data than try-in 1 in all criteria: muscle tone (respectively 71% vs. 59%), contraction synchrony (79% vs. 75%), contraction efficiency (85% vs. 77%), an increase in range of motion of 3.3 mm, and a better velocity (0.35 ± 0.12 s vs. 0.57 ± 0.14 s, p = 0.008). The piezographic impression, in combination with CAD-CAM, allowed the comparison of two prosthetic designs and the selection of the try-in with the best neuro-musculo-kinetic results.

Identifying homogeneous subgroups of patients and important features: a topological machine learning approach.

BACKGROUND: This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph. RESULTS: We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper. CONCLUSIONS: Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline .

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis.

MOTIVATION: Unbiased clustering methods are needed to analyze growing numbers of complex datasets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small datasets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that discerns additional feature and discovers hidden insights on datasets and has a wide application range. RESULTS: We have developed a topology-based clustering method called Two-Tier Mapper (TTMap) for enhanced analysis of global gene expression datasets. First, TTMap discerns divergent features in the control group, adjusts for them, and identifies outliers. Second, the deviation of each test sample from the control group in a high-dimensional space is computed, and the test samples are clustered using a new Mapper-based topological algorithm at two levels: a global tier and local tiers. All parameters are either carefully chosen or data-driven, avoiding any user-induced bias. The method is stable, different datasets can be combined for analysis, and significant subgroups can be identified. It outperforms current clustering methods in sensitivity and stability on synthetic and biological datasets, in particular when sample sizes are small; outcome is not affected by removal of control samples, by choice of normalization, or by subselection of data. TTMap is readily applicable to complex, highly variable biological samples and holds promise for personalized medicine. AVAILABILITY AND IMPLEMENTATION: TTMap is supplied as an R package in Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The prognostic significance of capsular incision into tumor during radical prostatectomy.

BACKGROUND: The prognostic significance of capsular incision (CapI) into tumor during radical prostatectomy (RP) with otherwise organ-confined disease remains uncertain. OBJECTIVE: To evaluate the impact of CapI into tumor on oncologic outcome. DESIGN, SETTING, AND PARTICIPANTS: A retrospective review of 8110 consecutive patients with prostate cancer treated at Ottawa Hospital and at Memorial Sloan-Kettering Cancer Center, both tertiary academic centers, between 1985 and 2008. INTERVENTION: All patients underwent an open, laparoscopic or robotic RP. MEASUREMENTS: Patients were divided into four pathologic categories: group 1 (CapI group), positive surgical margins (PSMs) without extraprostatic extension (EPE); group 2, negative surgical margins (NSMs) without EPE; group 3, NSM with EPE; group 4, PSMs with EPE. Estimates of recurrence-free survival were generated with the Kaplan-Meier method. Recurrence was defined as a prostate-specific antigen (PSA) >0.2 ng/ml and rising. Cox proportional hazards regression was used to estimate the hazard ratio (HR) for recurrence controlling for pretreatment PSA, RP date, RP Gleason sum, seminal vesicle invasion, and lymph node involvement. Pathologic categories were defined in the model by including the variables EPE and surgical margins (SMs) as well as their interaction. RESULTS AND LIMITATIONS: Median follow-up was 37.3 mo. The 5-yr recurrence-free probability after RP for the CapI group was 77% (95% confidence interval [CI], 72-83). This was not only inferior to patients with NSMs and no EPE (log rank p<0.0001) but also to those with NSMs and EPE (log rank p=0.0002). In multivariate analysis the interaction between EPE and SM was not significant (p=0.26). In the adjusted model excluding the interaction term, patients with EPE had an increased risk for recurrence (HR: 1.80; 95% CI, 1.49-2.17; p<0.0001) as did those with positive margins (HR: 1.81; 95% CI, 1.51-2.15; p<0.0001). This was a retrospective study. CONCLUSIONS: CapI into tumor has a significant impact on patient outcome following RP. Patients, who otherwise would have organ-confined disease, will now have a higher probability of recurrence than those with completely resected extraprostatic disease.