• MeSH
• Binomial Distribution MeSH
• Diagnosis statistics & numerical data   MeSH
• Statistics as Topic methods   MeSH

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

• MeSH
• Algorithms * MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Diagnosis, Differential MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Thyroid Gland diagnostic imaging   pathology   MeSH
• Support Vector Machine MeSH
• Ultrasonography methods   MeSH
• Thyroid Nodule classification   diagnostic imaging   pathology   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Silva invasion pattern can help predict lymph node metastasis risk in endocervical adenocarcinoma. We analysed Silva pattern of invasion and lymphovascular invasion to determine associations with clinical outcomes in stage IA and IB1 endocervical adenocarcinomas. International Federation of Gynecology and Obstetrics (FIGO; 2019 classification) stage IA-IB1 endocervical adenocarcinomas from 15 international institutions were examined for Silva pattern, presence of lymphovascular invasion, and other prognostic parameters. Lymph node metastasis status, local/distant recurrences, and survival data were compared using appropriate statistical tests. Of 399 tumours, 152 (38.1%) were stage IA [IA1, 77 (19.3%); IA2, 75 (18.8%)] and 247 (61.9%) were stage IB1. On multivariate analysis, lymphovascular invasion (p=0.008) and Silva pattern (p<0.001) were significant factors when comparing stage IA versus IB1 endocervical adenocarcinomas. Overall survival was significantly associated with lymph node metastasis (p=0.028); recurrence-free survival was significantly associated with lymphovascular invasion (p=0.002) and stage (1B1 versus 1A) (p=0.002). Five and 10 year overall survival and recurrence-free survival rates were similar among Silva pattern A cases and Silva pattern B cases without lymphovascular invasion (p=0.165 and p=0.171, respectively). Silva pattern and lymphovascular invasion are important prognostic factors in stage IA1-IB1 endocervical adenocarcinomas and can supplement 2019 International Federation of Gynecology and Obstetrics staging. Our binary Silva classification system groups patients into low risk (patterns A and B without lymphovascular invasion) and high risk (pattern B with lymphovascular invasion and pattern C) categories.

• MeSH
• Adenocarcinoma * pathology   MeSH
• Carcinoma * pathology   MeSH
• Humans MeSH
• Lymphatic Metastasis MeSH
• Uterine Cervical Neoplasms * MeSH
• Prognosis MeSH
• Retrospective Studies MeSH
• Neoplasm Staging MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.

• MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Diagnosis, Differential MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Sensitivity and Specificity MeSH
• Support Vector Machine * MeSH
• Ultrasonography methods   MeSH
• Thyroid Nodule diagnostic imaging   pathology   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Histopathological classification of basal cell carcinoma (BCC) has important prognostic and therapeutic implications, but reproducibility of BCC subtyping among dermatopathologists is poor. OBJECTIVES: To obtain a consensus paper on BCC classification and subtype definitions. METHODS: A panel of 12 recognized dermatopathologists (G12) from nine European countries used a modified Delphi method and evaluated 100 BCC cases uploaded to a website. The strategy involved five steps: (I) agreement on definitions for WHO 2018 BCC subtypes; (II) classification of 100 BCCs using the agreed definitions; (III) discussion on the weak points of the WHO classification and proposal of a new classification with clinical insights; (IV) re-evaluation of the 100 BCCs using the new classification; and (V) external independent evaluation by 10 experienced dermatopathologists (G10). RESULTS: A simplified classification unifying infiltrating, sclerosing, and micronodular BCCs into a single "infiltrative BCC" subtype improved reproducibility and was practical from a clinical standpoint. Fleiss' κ values increased for all subtypes, and the level of agreement improved from fair to moderate for the nodular and the unified infiltrative BCC groups, respectively. The agreement for basosquamous cell carcinoma remained fair, but κ values increased from 0.276 to 0.342. The results were similar for the G10 group. Delphi consensus was not achieved for the concept of trichoblastic carcinoma. In histopathological reports of BCC displaying multiple subtypes, only the most aggressive subtype should be mentioned, except superficial BCC involving margins. CONCLUSIONS: The three BCC subtypes with infiltrative growth pattern, characteristically associated with higher risk of deep involvement (infiltrating, sclerosing, and micronodular), should be unified in a single group. The concise and encompassing term "infiltrative BCCs" can be used for these tumors. A binary classification of BCC into low-risk and high-risk subtypes on histopathological grounds alone is questionable; correlation with clinical factors is necessary to determine BCC risk and therapeutic approach.

• MeSH
• Carcinoma, Basal Cell * pathology   MeSH
• Consensus MeSH
• Humans MeSH
• Skin Neoplasms * pathology   MeSH
• Reproducibility of Results MeSH
• Margins of Excision MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Cyanobacteria represent a bacterial phyllum characteristic by the ability to photosynthesize. They are potentially applicable for the production of useful compounds but may also cause poisoning or at least health problems as they can produce cyanotoxins. The introduction of a fast methodology is important not only for fundamental taxonomic purposes, but also for reliable identifications in biological studies. In this work, we have used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry of intact cells to study Chroococcidiopsis strains. A library of the obtained reference mass spectra containing characteristic peptide/protein profiles was examined by software tools to characterize similarities and differences applicable for diagnostics and taxonomy. Both a similarity tree and heat map constructed from the mass spectrometric data proved consistent with 16S rRNA sequencing results. We show as novelty that a binary matrix combining ferulic and sinapinic acids performs well in acquiring reproducible mass spectra of cyanobacteria. Using the matrix solvent, a protein extraction from cells was done. After polyacrylamide gel electrophoresis, the separated protein fractions were in-gel digested and the resulting peptides analyzed by liquid chromatography coupled with tandem mass spectrometry. For the first time, photosystem protein components, phycobilisome proteins, electron transport proteins, nitrogen-metabolism and nucleic acids binding-proteins, cytochromes plus other enzymes and various uncharacterized proteins could be assigned to characteristic peaks in the mass spectrometric profiles and some of them suggested as markers in addition to 30S and 50S ribosomal proteins known from previous studies employing intact cell mass spectrometry of microorganisms.

• MeSH
• Bacterial Proteins analysis   isolation & purification   MeSH
• Electrophoresis, Polyacrylamide Gel MeSH
• Phylogeny MeSH
• Peptides analysis   isolation & purification   MeSH
• RNA, Ribosomal, 16S genetics   MeSH
• Cyanobacteria chemistry   classification   genetics   MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles-the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps-in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers-logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient's health.

• Publication type
• Journal Article MeSH

• MeSH
• Clinical Medicine MeSH
• Statistics as Topic MeSH
• Publication type
• Abstracts MeSH
• Congress MeSH
• Collected Work MeSH
• News MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• statistika, zdravotnická statistika
• lékařství

• MeSH
• Biometry methods   MeSH
• Statistics as Topic MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Demografie. Populace
• NML Fields
• demografie
• statistika, zdravotnická statistika

• MeSH
• Meta-Analysis as Topic * MeSH
• Statistics as Topic MeSH
• Publication type
• Meta-Analysis MeSH
• Handbook MeSH

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika