Editorial Board

Publication date: November–December 2018

Source: Journal of Communication Disorders, Volume 76

Author(s):

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Immediate Effects of the Semi-Occluded Ventilation Mask on Subjects Diagnosed With Functional Dysphonia and Subjects With Normal Voices

Publication date: Available online 10 November 2018

Source: Journal of Voice

Author(s): Kharina Frisancho, Lukas Salfate, Karla Lizana, Marco Guzman, Fernando Leiva, Camilo Quezada

Abstract

Purpose

The present study was designed to assess the immediate effects of the semi-occluded ventilation mask (SOVM) in subjects with functional dysphonia and subjects with normal voice.

Methods

Sixty-four participants were included in this study (48 women and 16 men). Thirty-one of them were diagnosed with functional dysphonia and 33 with normal voice. All subjects were randomly assigned to one of two conditions: an experimental condition using the SOVM (n = 33) and a control condition with participants not using the SOVM (n = 31). Thus, within both conditions, participants could be either dysphonic or normal-voiced. This produced a total of four different groups: (1) subjects with normal voice with SOVM (n = 17), (2) subjects with normal voice without SOVM (n = 16), (3) dysphonic subjects with SOVM (n = 16), and (4) dysphonic subjects without SOVM (n = 15). All participants underwent aerodynamic, electroglottographic (EGG), and acoustic assessments, and were also asked to assess their own voice, before and after voice exercises.

Results

Significant differences were found for aerodynamic, EGG, and acoustic variables when comparing SOVM conditions (dysphonic and normal) against control. Cepstral peak prominence and EGG contact quotient showed an increase among dysphonic participants with SOVM. L1-L0 showed an increase for all participants in SOVM condition (dysphonic and normal). Self-perceived resonant voice quality showed an increase for both groups in SOVM condition. Glottal airflow showed a decrease for the dysphonic participants in SOVM condition. Phonation threshold pressure and subglottic pressure showed a decrease for both groups in SOVM condition.

Conclusion

The present study suggests that immediate positive effect could be produced by connected speech phonatory tasks using the SOVM in both dysphonic subjects and subjects with normal voice, the change being greater among the former. SOVM seems to promote an easy voice production and a more efficient phonation.

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Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Publication date: Available online 10 November 2018

Source: Academic Radiology

Author(s): Kenny H. Cha, Lubomir M. Hadjiiski, PhD, Richard H. Cohan, MD, Heang-Ping Chan, PhD, Elaine M. Caoili, MD, Matthew Davenport, MD, Ravi K. Samala, PhD, Alon Z. Weizer, MD, Ajjai Alva, MD, Galina Kirova-Nedyalkova, MD, PhD, Kimberly Shampain, MD, Nathaniel Meyer, MD, Daniel Barkmeier, MD, PhD, Sean Woolen, MD, Prasad R. Shankar, MD, Isaac R. Francis, MD, Phillip Palmbos, MD

Rationale and Objectives

To evaluate whether a computed tomography (CT)-based computerized decision-support system for muscle-invasive bladder cancer treatment response assessment (CDSS-T) can improve identification of patients who have responded completely to neoadjuvant chemotherapy.

Materials and Methods

Following Institutional Review Board approval, pre-chemotherapy and post-chemotherapy CT scans of 123 subjects with 157 muscle-invasive bladder cancer foci were collected retrospectively. CT data were analyzed with a CDSS-T that uses a combination of deep-learning convolutional neural network and radiomic features to distinguish muscle-invasive bladder cancers that have fully responded to neoadjuvant treatment from those that have not. Leave-one-case-out cross-validation was used to minimize overfitting. Five attending abdominal radiologists, four diagnostic radiology residents, two attending oncologists, and one attending urologist estimated the likelihood of pathologic T0 disease (complete response) by viewing paired pre/post-treatment CT scans placed side-by-side on an internally-developed graphical user interface. The observers provided an estimate without use of CDSS-T and then were permitted to revise their estimate after a CDSS-T-derived likelihood score was displayed. Observer estimates were analyzed with multi-reader, multi-case receiver operating characteristic methodology. The area under the curve (AUC) and the statistical significance of the difference were estimated.

Results

The mean AUCs for assessment of pathologic T0 disease were 0.80 for CDSS-T alone, 0.74 for physicians not using CDSS-T, and 0.77 for physicians using CDSS-T. The increase in the physicians' performance was statistically significant (P < .05).

Conclusion

CDSS-T improves physician performance for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

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Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy

Publication date: Available online 10 November 2018

Source: Academic Radiology

Author(s): Jing Zhang, Xinjie Liu, Haiping Zhang, Xiaojing He, Yangyang Liu, Jun Zhou, Dajing Guo

Rationale and Objectives

To investigate the value of texture analysis and conventional magnetic resonance imaging (MRI) features for predicting the early recurrence (ER) of single hepatocellular carcinoma (HCC) after hepatectomy.

Materials and Methods

A total of 100 HCC patients were first divided into group A (tumor diameter ≤3 cm) and group B (tumor diameter >3 cm) and then classified into two subgroups with ER or nonearly recurrence. Textural parameters (skewness, kurtosis, uniformity, energy, entropy, and correlation) based on MR images and conventional MRI features were compared between the ER and nonearly recurrence subgroups. Predictive factors for ER were further assessed with multivariate logistic regression analysis. Receiver operating characteristic curve was performed to assess the predictive power.

Results

There were 53 patients in group A and 47 patients in group B. On arterial phase analysis, tumors with ER displayed significantly lower uniformity and higher entropy in group A, and higher skewness and entropy in group B. On portal venous phase analysis, tumors with ER had significantly lower kurtosis and energy in group A, and higher entropy in group B. Irregular margin in groups A and B, and arterial peritumoral enhancement and capsule presence in group B were associated with ER. In multivariate logistic regression analysis, uniformity and entropy based on arterial phase images and irregular margin in group A, and skewness and entropy based on arterial phase images and arterial peritumoral enhancement in group B were independent predictors for ER. Entropy displayed higher predictive power for ER.

Conclusion

Texture analysis based on preoperative MRI are potential quantitative predictors of ER in HCC patients after hepatectomy, and may provide more information for preoperative treatment decision-making and follow up.

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Disease Quantification on PET/CT Images without Explicit Object Delineation

Publication date: Available online 10 November 2018

Source: Medical Image Analysis

Author(s): Yubing Tong, Jayaram K. Udupa, Dewey Odhner, Caiyun Wu, Stephen J. Schuster, Drew A. Torigian

Abstract

Purpose

The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e.g., organ) delineation through the use of only object recognition results from our recently developed automatic anatomy recognition (AAR) method to quantify disease burden.

Method

The AAR-DQ process starts off with the AAR approach for modeling anatomy and automatically recognizing objects on low-dose CT images of PET/CT acquisitions. It incorporates novel aspects of model building that relate to finding an optimal disease map for each organ. The parameters of the disease map are estimated from a set of training image data sets including normal subjects and patients with metastatic cancer. The result of recognition for an object on a patient image is the location of a fuzzy model for the object which is optimally adjusted for the image. The model is used as a fuzzy mask on the PET image for estimating a fuzzy disease map for the specific patient and subsequently for quantifying disease based on this map. This process handles blur arising in PET images from partial volume effect entirely through accurate fuzzy mapping to account for heterogeneity and gradation of disease content at the voxel level without explicitly performing correction for the partial volume effect. Disease quantification is performed from the fuzzy disease map in terms of total lesion glycolysis (TLG) and standardized uptake value (SUV) statistics. We also demonstrate that the method of disease quantification is applicable even when the "object" of interest is recognized manually with a simple and quick action such as interactively specifying a 3D box ROI. Depending on the degree of automaticity for object and lesion recognition on PET/CT, DQ can be performed at the object level either semi-automatically (DQ-MO) or automatically (DQ-AO), or at the lesion level either semi-automatically (DQ-ML) or automatically.

Results

We utilized 67 data sets in total: 16 normal data sets used for model building, and 20 phantom data sets plus 31 patient data sets (with various types of metastatic cancer) used for testing the three methods DQ-AO, DQ-MO, and DQ-ML. The parameters of the disease map were estimated using the leave-one-out strategy. The organs of focus were left and right lungs and liver, and the disease quantities measured were TLG, SUV_Mean, and SUV_Max. On phantom data sets, overall error for the three parameters were approximately 6%, 3%, and 0%, respectively, with TLG error varying from 2% for large "lesions" (37 mm diameter) to 37% for small "lesions" (10 mm diameter). On patient data sets, for non-conspicuous lesions, those overall errors were approximately 19%, 14% and 0%; for conspicuous lesions, these overall errors were approximately 9%, 7%, 0%, respectively, with errors in estimation being generally smaller for liver than for lungs, although without statistical significance.

Conclusions

Accurate disease quantification on PET/CT images without performing explicit delineation of lesions is feasible following object recognition. Method DQ-MO generally yields more accurate results than DQ-AO although the difference is statistically not significant. Compared to current methods from the literature, almost all of which focus only on lesion-level DQ and not organ-level DQ, our results were comparable for large lesions and were superior for smaller lesions, with less demand on training data and computational resources. DQ-AO and even DQ-MO seem to have the potential for quantifying disease burden body-wide routinely via the AAR-DQ approach.

Graphic Abstract

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Assessing the osteogenic potential of zirconia and titanium surfaces with an advanced in vitro model

Publication date: Available online 10 November 2018

Source: Dental Materials

Author(s): Markus Rottmar, Eike Müller, Stefanie Guimond-Lischer, Marc Stephan, Simon Berner, Katharina Maniura-Weber

Abstract

Objectives

In recent years, zirconia dental implants have gained increased attention especially for patients with thin gingival biotypes or patients seeking metal-free restoration. While physical and chemical material surface properties govern the blood-material interaction and subsequent osseointegration processes, the organizational principles underlying the interplay of biochemical and biophysical cues are still not well understood. Therefore, this study investigated how the interaction of a microstructured zirconia surface with blood influences its osseointegration potential compared to microstructured titanium with or without additional nanostructures.

Methods

Microstructured zirconia and micro- (and nano)structured titanium surfaces were fabricated via sandblasting followed by acid etching and their topographical as well as physico-chemical features were thoroughly characterized. Following, an advanced in vitro approach mimicking the initial blood interaction of material surfaces upon implantation was applied. Fibrinogen adsorption, human blood coagulation as well as their influence on cell fate decisions of primary human bone and progenitor cells (HBC) were studied.

Results

Obtained surface micro- and nanostructures on titanium surfaces were sharp with rugged peaks whereas zirconia surfaces were less rough with structures being shallower, more round and granular. Compared to titanium surfaces, the zirconia surface showed increased fibrinogen adsorption, higher levels of total accessible fibrinogen γ-chain moieties yielding in increased platelet adhesion and activation and consequently thrombogenicity. Mineralization of HBC on microstructured surfaces was significantly higher on zirconia than on titanium, but was significantly lower compared to titanium surfaces with nanostructures.

Significance

This study provides insights into blood-material interaction and subsequent cellular events that are important for implant surface development.

Graphical abstract

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Calcium phosphate coatings elaborated by the soaking process on titanium dental implants: Surface preparation, processing and physical–chemical characterization

Publication date: Available online 10 November 2018

Source: Dental Materials

Author(s): Camille Pierre, Ghislaine Bertrand, Christian Rey, Olivier Benhamou, Christèle Combes

Abstract

Objective

Dental implant manufacturers are looking for new surfaces to improve osseointegration. It is accepted that calcium phosphate coatings favor bone healing. Among all the techniques, the soaking process seems attractive because of its ability in producing a bioactive coating at low temperature. The objective of this study is to improve the titanium implant surface roughness and chemistry by optimizing the surface preparation and the soaking process parameters to produce a bioactive and adherent calcium phosphate coating.

Methods

Titanium samples were sandblasted and acid etched. Coatings were realized by an alternate soaking process including a centrifugation step to create a phosphate solution thin film on the implant that reacts with the calcium of the second bath. We performed a characterization of the sample surface with complementary physical and physico-chemical techniques to assess the effect of surface preparation and coating process operating parameters on coating formation and characteristics.

Results

Surface preparation led to a roughness around 1.6 μm, micro-porosities, high surface wettability and removed the embedded sandblasting particles. We showed that the centrifugation step is critical and determines the coating formation, coverage and thickness. A thin coating (∼2 μm) composed of apatite analogous to bone mineral was deposited. The coating adhesion was demonstrated by screwing/unscrewing test in an artificial jawbone.

Significance

The titanium dental implant pre-treatment and coating developed in this study is expected to favor early implant osseointegration through coating dissolution in vivo and could be associated with biological active agents to confer additional functionality to the coating.

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