Davisrowland4153

Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas METHODS Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance.

Fifty-two studies were included involving soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 sarcoma radiomics studies and accelerate clinical applications.

This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer.

A total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. Histological verification was based on the surgical specimens. CT findings were determined by a panel of three radiologists. Conventional hand-crafted features and deep learning features were extracted from three phases CT images and were utilized to build radiomics signatures via machine learning methods. Incorporating the radiomics signatures and CT findings, a radiomics nomogram was developed via multivariable logistic regression. Its diagnostic ability was measured using receiver operating characteristiccurve analysis.

The radiomics signatures, built with support vector machine or artificial neural network, showed good performance for discriminating T4a in the test I and II sets with area under curves (AUCs) of 0.76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values < 0.05).

The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.

The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.

Bone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI).

In this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 73 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion.

Eight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660-0.768] and 0.722 [95 % CI, 0.668-0.776] and in the test datasets of 0.715 [95 % CI, 0.632-0.798] and 0.713 [95 % CI, 0.628-0.798], respectively.

The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.

The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.

The study aims to determine the diagnostic value of high-resolution ultrasonography (US) compared with magnetic resonance imaging (MRI) for the evaluation of temporomandibular disorders (TMD).

Fifty consecutive patients (42 female and 8 male) with signs and symptoms of TMD according to the Research Diagnostic Criteria for TMD were enrolled in the study. Each patient underwent US (13 and 20 MHz) and MRI examination of both TMJs, 1-7 days following clinical examination. All MRI examinations were performed by another radiologist using an 1.5 T MRI device. Sensitivity (Se), specificity (Sp), positive and negative predictive values (PPV, NPV) and diagnostic accuracy were computed along with 95% confidence intervals.

For overall disc displacements, 13 MHz US showed a Se of 72.58%, Sp of 86.84%, PPV of 90%, NPV of 66% and diagnostic accuracy of 78%, while 20 MHz US showed a Se of 75.81%, Sp of 86.84%, PPV of 90.38%, NPV of 68.75% and a diagnostic accuracy of 80%. For degenerative changes, 13 MHz US revealed a Se of 58.33%, Sp of 92.11%, PPV of 70%, NPV of 87.5% and a diagnostic accuracy of 84%, whereas 20 MHz US indicated the same Se of 58.33%, Sp of 93.42%, PPV of 73.68%, NPV of 87.65% and a diagnostic accuracy of 85%. The Cohen's Kappa coefficient for the intra- and inter-observer agreement was 0.822 and 0.836 for disc displacement, respectively 0.813 and 0.788 for degenerative disorders (p < 0.001).

High-resolution US could be a useful imaging technique in diagnosing TMJ disc displacements.

High-resolution US could be a useful imaging technique in diagnosing TMJ disc displacements.The change in quality of quick-frozen patties containing different amounts of added fat (0%, 5%, 10%, 15%, and 20%) under different freeze-thaw (F-T) cycles (a F-T cycle was performed by freezing at -18 °C and thawing at 4 °C) was evaluated. The results showed that the a*-values of samples were significantly decreased, while L*-values, b*-values, thawing loss, and cooking loss were notably increased after 3 F-T cycles. Low-field nuclear magnetic resonance (LF-NMR) results showed that the water mobility of patties was enhanced. Textural properties (hardness, springiness, cohesiveness, and chewiness) of patties were significantly decreased after 5 F-T cycles (P less then 0.05). Lipid and protein oxidation were aggravated with increasing fat content and number of F-T cycles, as confirmed by the increase in lipid peroxides, TBARS, and carbonyl content. Therefore, the results from instrument-based detection and consumer scores indicated that repeated F-T cycles accelerated the quality deterioration of quick-frozen pork patties, and rendered them unacceptable after 3 F-T cycles.In this study, ɛ-polylysine (ɛ-PL) or ɛ-polylysine nanoparticle (ɛ-PLN) combined with plants extracts (including green tea, olive leaves and stinging nettle extracts) were used as nitrite replacers in frankfurter-type sausages. The sausage samples were wrapped in polyethylene bags (in vacuum conditions) and their physicochemical, microbiological and sensory properties were evaluated during 45 days of refrigerated storage. The results showed that the incorporation of ɛ-polylysine had no significant effects on proximate composition of sausages. However, ɛ-PL and ɛ-PLN sausages had significantly (P less then 0.05) lower lightness, redness and higher yellowness compared to control samples. At the end of storage, sausages formulated with ɛ-PLN had significantly (P less then 0.05) higher contents of phenolic compounds and lowest TBARS values. Microbiological counts also indicated that ɛ-PLN displayed significantly higher inhibitory effects. selleck Higher sensory indices were obtained in ɛ-PLN sausages. Based on the obtained results, ɛ-PLN was effective to improve frankfurter-type sausages shelf life. Therefore, these ingredients could be useful for frankfurter-type sausages production as nitrite replacers.Given the more recent interest in its flavour enhancing potential, the effects of the addition of glucosamine or glucosamine caramel on both technological and consumer acceptability of regular and reduced salt breakfast sausages were studied. A 2 × 3 complete factorial design was used with salt level (regular salt, RS (1.1%) and low salt, LS (0.825%)) and formulation treatment (control, GlcN - glucosamine (1%), CAR - glucosamine caramel (1% GlcN equivalent)) as main effects. Raw or cooked sausages were analyzed for CIE L*, a* and b*, physical and textural properties and consumer acceptance. Different salt levels did not affect the pH of meat batter, while the reduced salt treatment resulted in higher cook loss. On the contrary, addition of GlcN and CAR reduced the pH of sausage with no effect on cook loss. Neither salt levels nor treatment formulation affected the textural attributes of sausages. The inclusion of CAR significantly reduced L* value and increased redness (a*) and yellowness (b*) of cooked sausages. Salt reduction resulted in decreased a* and b* values in raw batter; the effect which disappeared in cooked sausages. Glucosamine caramel increased the overall and flavour acceptability score of low salt breakfast sausages. The present study showed that glucosamine caramel could potentially improve the flavour of low salt breakfast sausage with limited effect on textural attributes.The Spanish market offers a greater variety of Iberian pork products. The aim of this paper is to determine the perception of consumers of several aspects of Iberian pig production and animal welfare depending on the consumers' characteristics. Consumers from two Spanish regions (n = 403) answered a questionnaire about their beliefs and the importance of pig production, their purchase intentions and their willingness to pay. Consumers were segmented according to their level of knowledge about Iberian pig production. The results of this work indicate that consumers have poor knowledge about Iberian pig production. Even so, consumers show a remarkable preference for Iberian products, especially when the animals are reared freely and in natural conditions, giving great importance to animal welfare. Consumer preferences indicate the importance of emphasizing Iberian traditional pig product characteristics on the label to promote their purchase choices.For evidence evaluation of the physicochemical properties of glass at activity level a well-known formula introduced by Evett & Buckleton [1,2] is commonly used. Parameters in this formula are, amongst others, the probability in a background population to find on somebody's clothing the observed number of glass sources and the probability in a background population to find on somebody's clothing a group of fragments with the same size as the observed matching group. Recently, for efficiency reasons, the Netherlands Forensic Institute changed its methodology to measure not all the glass fragments but a subset of glass fragments found on clothing. Due to the measurement of subsets, it is difficult to get accurate estimates for these parameters in this formula. We offer a solution to this problem. The heart of the solution consists of relaxing the assumption of conditional independence of group sizes of background fragments, and modelling the probability of an allocation of background fragments into groups given a total number of background fragments by a two-parameter Chinese restaurant process (CRP) [3].