23 research outputs found
A homogeneous space of point-countable but not of countable type
No full textsummary:We construct an example of a homogeneous space which is of point-countable but not of countable type. This shows that a result of Pasynkov cannot be generalized from topological groups to homogeneous spaces
Perfect compactifications of functions
No full textsummary:We prove that the maximal Hausdorff compactification of a -compactifi\-able mapping and the maximal Tychonoff compactification of a Tychonoff mapping (see [P]) are perfect. This allows us to give a characterization of all perfect Hausdorff (respectively, all perfect Tychonoff) compactifications of a -compactifiable (respectively, of a Tychonoff) mapping, which is a generalization of two results of Skljarenko [S] for the Hausdorff compactifications of Tychonoff spaces
Prostate cancer morbidity in the Mari El Republic: A retrospective observational study
No full textBackground. Prostate cancer maintains a relatively high standardized uptake value and share of patients followed up for 5 or more years. Accordingly, distant outcomes in these patients appear to be influenced by factors other than the underlying disease.Objective. To analyze the morbidity in prostate cancer patients with additional malignancies potentially linked with the decrease in the survival rate in the Mari El Republic.Methods. The present study involved 1434 prostate cancer patients firstly enrolled in the period from 2012 to 2021. A group of patients in this sample was identified with additional malignancies (other than prostate cancer) diagnosed within the period from 6 months prior to prostate cancer diagnosis to the end of 2021. Comparison of the incidence of malignancies among prostate cancer patients and the general population was performed via a 2 × 2 crosstab analysis by calculating the relative risk and its 95% confidence interval. The difference was considered significant when 95% confidence interval did not include 1. In addition, chi-square values and corresponding p-values were calculated. Statistical analyses were performed using SPSS 13.0 (SPSS Inc., USA) and Microsoft Excel 2007 (Microsoft Corporation, USA).Results. 31 (32.29%) additional malignancies were identified (prostate cancer was diagnosed within 6 months before prostate cancer diagnosis and up to 6 months thereafter), 7 additional malignancies (7.29%) were registered 6 months to 1 year after prostate cancer diagnosis, and 61 additional malignancies (63.54%) during the later period. The most common primary malignancies among all patients included: bladder cancer (relative risk = 15.23 [95% confidence interval: 10.42–22.26]), nonmelanoma skin cancer (relative risk = 3.77 [2.34–6.07]), colorectal cancer (relative risk = 2.10 [1.24–3.54]), gastric cancer (relative risk = 2.01 [1.08–3.73]), and kidney cancer (relative risk = 4.69 [2.51–8.75]).Conclusion. Within 7.1 years (median) of follow-up, additional malignancies develop in 6.70% of prostate cancer patients. These patients reveal the higher risk than the population average value, thereby constituting a risk group
Application of fully convolutional neural networks to mapping industrial oil palm plantations
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Grayscale Color Mapping with the Mathematical Analysis of an Ultrasound Image in the Differential Diagnosis of Cystic and Solid Breast Masses
No full textObjective. Atypical breast cysts are often quite a serious problem in noninvasive ultrasound differential diagnosis. To develop a system for automated analysis of grayscale ultrasound images, which on the principles of mathematical processing would make it possible to increase the specificity of diagnosis in this situation.Material and methods. The authors developed the CystChecker 1.0 software package. To test this system, they used a set of 217 ultrasound images: 107 cystic (including 53 atypical lesions that were hardly differentially diagnosed by standard methods) and 110 solid (both benign and malignant) breast masses. All the masses were verified by cytology and/or histology. Visual assessment was carried out analyzing grayscale ultrasound, color/power Doppler, and elastography images.Results. Using the system developed by the authors could correctly identify all (n = 107 (100%)) typical cysts, 107 (97.3%) of 110 solid masses, and 50 (94.3%) of 53 atypical cysts. On the contrary, the standard visual assessment provided a possibility of correctly identifying all (n = 107 (100%)) typical cysts, 96 (87.3%) of 110 solid masses, and 32 (60.4%) of 53 atypical cysts (p < 0.05). The corresponding values of the overall specificity of automated and visual assessments were 98 and 87%, respectively.Conclusion. Using the system developed by the authors for automated analysis provides a higher specificity than the visual assessment of an ultrasound image, which is carried out by a qualified specialist
Сравнительный анализ диагностической ценности систем компьютерного анализа маммограмм I и II поколений
No full textAim: to compare the diagnostic efficacy of generation I and II computer aided detection (CAD) systems for mammography of our own design using the large set of unselect ed mammography images obtained in a routine clinical practice settings. Material and methods. Both CADs were tested on the set of 1532 mammography images of 356 women with confirmed breast cancer (BC). We assessed their value in the detection of suspicious areas with various characteristics located on the different density background. Size of BC lesions varied from 4 to 35 mm (mean – 13,4 ± 6,3 mm). We excluded BC representing only with microcalcification clusters from this analysis, because this task is solved using the separate universal module compatible with both CADs.Results. For I and II generation CADs we obtained the following results: detection of small nodular BCs (≤10 mm) – 41 of 52 (78.85%) and 48 of 52 (92.31%; p > 0.05), respectively; detection of BCs visible as asymmetric areas – 18 of 18 (100%) and 13 of 18 (72.2%; p > 0.05), respectively; detection of only partially visible masses – 15 of 18 (83.3%) and 17 of 18 (94.4%; p > 0.05); detection of lesions poorly visible or invisible on standard mammography images due to the high density background (C-D types according to the ACR 2013 classification) – 9 of 16 (56.3%) and 7 of 16 (70.0%; p = 0.046). Total detection rate was 88.76% (316 of 356 cases) – for CAD I and 90.73% (323 of 356 cases; р > 0.05) – for CAD II. Mean false positive marks rate was 1.8 and 1.3 per image, respectively, – for ACR А-В images and 2.6 and 1.8 per image, respectively – for ACR C-D images (p < 0.05).Conclusion. Generally the diagnostic value of CAD II is not inferior that of CAD I in all analyzed situations, except the poorly visible or invisible lesions on the dense breast background. Moreover, CAD II is probably superior CAD I in the detection of spiculated small masses. The rate of false positive marks was significantly higher for CAD I.Цель исследования: сравнительная оценка эффективности работы систем компьютерного анализа (CAD) I и II поколений собственной разработки на обширной базе неотобранных маммографических изображений, полученных в условиях рутинной клинической практики.Материал и методы. Обе системы были протестированы на наборе из 1532 маммограмм 356 пациенток с верифицированным раком молочной железы (РМЖ) на способность обнаруживать подозрительные области с различными характеристиками на маммограммах различной степени плотности. Размер образований, соответствовавших РМЖ, варьировал от 4 до 35 мм (средний – 13,4 ± 6,3 мм). Исключали случаи РМЖ, проявлявшиеся только в виде скоплений микрокальцинатов, поскольку данная задача решается с использованием отдельного универсального блока.Результаты. При использовании систем I и II поколения были получены следующие результаты соответственно: обнаружение малых раков (до 10 мм) с очаговым ростом – 41 (78,85%) из 52 и 48 (92,31%; p > 0,05) из 52; обнаружение РМЖ, проявляющегося в виде асимметрии, – 18 (100%) из 18 и 13 (72,2%; p > 0,05) из 18; обнаружение частично срезанных образований – 15 (83,3%) из 18 и 17 (94,4%; p > 0,05) из 18; обнаружение образований, плохо видимых или вообще невидимых на стандартных маммограммах ввиду плотной паренхимы МЖ (типы C-D согласно ACR 2013), – 9 (56,3%) из 16 и 7 (70,0%; p = 0,046) из 16. Общая частота обнаружения подозрительных образований составила 88,76% (316 из 356 случаев) – для CAD I и 90,73% (323 из 356 случаев; р>0,05) – для CAD II. Частота ложноположительных меток составила в среднем 1,8 и 1,3 соответственно на маммограмму при типах ACR А–В и 2,6 и 1,8 соответственно – при типах ACR C–D (p < 0,05).Выводы. Эффективность CAD II сравнима с таковой CAD I во всех ситуациях, за исключением выявления плохо видимых и невидимых образований вследствие плотной паренхимы МЖ. Кроме того, CAD II, вероятно, превосходит CAD I в выявлении спикулизированных образований малых размеров. Частота ложноположительных меток при использовании CAD I была достоверно выше
Čech-completeness and ultracompleteness in “nice spaces”
No full textsummary:We prove that if is a union of subspaces of pointwise countable type then the space is of pointwise countable type. If is a countable union of ultracomplete spaces, the space is ultracomplete. We give, under CH, an example of a Čech-complete, countably compact and non-ultracomplete space, giving thus a partial answer to a question asked in [BY2]
