28 research outputs found
Prostate cancer morbidity in the Mari El Republic: A retrospective observational study
Background. 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 Γ 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: 10.42β22.26]), nonmelanoma skin cancer (relative risk = 3.77 [2.34β6.07]), colorectal cancer (relative risk = 2.10 [1.24β3.54]), gastric cancer (relative risk = 2.01 [1.08β3.73]), and kidney cancer (relative risk = 4.69 [2.51β8.75]).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
ΠΠΎΠ²ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π»ΡΡΠ΅Π²ΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π² ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ Π·Π° ΡΠ΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ΅ΠΉ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ°Ρ
Objective: comparative study of the possibilities of radiation methods in control of bone regeneration in fractures.Materials and methods. A study of bone calluse formation was conducted in 116 patients with broken tubular bones of the upper and lower limbs from the first day of the fracture to the complete consolidation of the fractures. In X-rays and CT determined the mineral density of bone calluses, with ultrasonic elastography shear wave and compression elastography determined the rigidity of bone calluses in kPa.Results. X-ray bone corn was determined at the end of the second β the beginning of the third stage of bone calluse formation. On CT bone corn density (in the HU), its structure and the condition of bone breaks were determined in some patients in all stages of bone regenerate formation. In ultrasound examination in the multiparametric mode studied the rigidity, structure and vascularization of bone calluses, the correct comparison of bone breaks from the first day of the fracture to their complete fusion.Conclusions. Ultrasonic studies in multiparametric mode can be used to control bone regeneration in fractures. Ultrasonic elastography shear wave, in determining the rigidity of bone calluse, in sensitivity and specificity exceeds the indicators of ultrasonic compression elastography at all stages of bone regenerate formation.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π»ΡΡΠ΅Π²ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π² ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ Π·Π° ΡΠ΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ΅ΠΉ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ°Ρ
.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ Ρ 116 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ°ΠΌΠΈ ΡΡΡΠ±ΡΠ°ΡΡΡ
ΠΊΠΎΡΡΠ΅ΠΉ Π²Π΅ΡΡ
Π½ΠΈΡ
ΠΈ Π½ΠΈΠΆΠ½ΠΈΡ
ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΡΡΠ΅ΠΉ Ρ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ Π΄Π½Ρ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ° Π΄ΠΎ ΠΏΠΎΠ»Π½ΠΎΠΉ ΠΊΠΎΠ½ΡΠΎΠ»ΠΈΠ΄Π°ΡΠΈΠΈ ΠΎΡΠ»ΠΎΠΌΠΊΠΎΠ². ΠΡΠΈ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΈ ΠΠ’ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΡΡ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ, ΠΏΡΠΈ ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠΉ ΡΠ»Π°ΡΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΠ΄Π²ΠΈΠ³ΠΎΠ²ΠΎΠΉ Π²ΠΎΠ»Π½ΠΎΠΉ ΠΈ ΠΊΠΎΠΌΠΏΡΠ΅ΡΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ»Π°ΡΡΠΎΠ³ΡΠ°ΡΠΈΠΈ β ΠΆΠ΅ΡΡΠΊΠΎΡΡΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ Π² ΠΊΠΈΠ»ΠΎΠΏΠ°ΡΠΊΠ°Π»ΡΡ
.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π Π΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ ΠΊΠΎΡΡΠ½Π°Ρ ΠΌΠΎΠ·ΠΎΠ»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»Π°ΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ Π²ΡΠΎΡΠΎΠΉ β Π½Π°ΡΠ°Π»Π΅ ΡΡΠ΅ΡΡΠ΅ΠΉ ΡΡΠ°Π΄ΠΈΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ. ΠΠ° ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ (Π² Π΅Π΄ΠΈΠ½ΠΈΡΠ°Ρ
Π₯Π°ΡΠ½ΡΡΠΈΠ»Π΄Π°), Π΅Π΅ ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΠΊΠΎΡΡΠ½ΡΡ
ΠΎΡΠ»ΠΎΠΌΠΊΠΎΠ² ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ Ρ ΡΠ°ΡΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π²ΠΎ Π²ΡΠ΅Ρ
ΡΡΠ°Π΄ΠΈΡΡ
ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π³Π΅Π½Π΅ΡΠ°ΡΠ°. ΠΡΠΈ ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ Π² ΠΌΡΠ»ΡΡΠΈΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΈΠ·ΡΡΠ°Π»ΠΈ ΠΆΠ΅ΡΡΠΊΠΎΡΡΡ, ΡΡΡΡΠΊΡΡΡΡ ΠΈ Π²Π°ΡΠΊΡΠ»ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ, ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΠΎΡΡΡ ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΊΠΎΡΡΠ½ΡΡ
ΠΎΡΠ»ΠΎΠΌΠΊΠΎΠ² Ρ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ Π΄Π½Ρ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ° ΠΈ Π΄ΠΎ ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ ΠΈΡ
ΡΡΠ°ΡΠ΅Π½ΠΈΡ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π£Π»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΌΡΠ»ΡΡΠΈΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΡ Π΄Π»Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π·Π° ΡΠ΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ΅ΠΉ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠ°Ρ
. Π£Π»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²Π°Ρ ΡΠ»Π°ΡΡΠΎΠ³ΡΠ°ΡΠΈΡ ΡΠ΄Π²ΠΈΠ³ΠΎΠ²ΠΎΠΉ Π²ΠΎΠ»Π½ΠΎΠΉ ΠΏΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΠΆΠ΅ΡΡΠΊΠΎΡΡΠΈ ΠΊΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ·ΠΎΠ»ΠΈ ΠΏΠΎ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π²ΡΡΠ°Π΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΡΠ΅ΡΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ»Π°ΡΡΠΎΠ³ΡΠ°ΡΠΈΠΈ Π½Π° Π²ΡΠ΅Ρ
ΡΡΠ°Π΄ΠΈΡΡ
ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π³Π΅Π½Π΅ΡΠ°ΡΠ°
Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΡΡΠ΅ΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΠΌΠΌ I ΠΈ II ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ
Aim: to compare the diagnostic efficacy of generationΒ I and II computer aided detection (CAD) systems for mammographyΒ of our own design using the large set of unselect ed mammography images obtained in a routine clinical practice settings.Β Material and methods. Both CADs were tested on theΒ set of 1532 mammography images of 356 women with confirmedΒ breast cancer (BC). We assessed their value in theΒ detection of suspicious areas with various characteristicsΒ located on the different density background. Size of BCΒ lesions varied from 4 to 35 mm (mean β 13,4 Β± 6,3 mm).Β We excluded BC representing only with microcalcificationΒ clusters from this analysis, because this task is solved usingΒ the separate universal module compatible with both CADs.Results. For I and II generation CADs we obtained theΒ following results: detection of small nodular BCs (β€10 mm) βΒ 41 of 52 (78.85%) and 48 of 52 (92.31%; p > 0.05), respectively;Β detection of BCs visible as asymmetric areas β 18 ofΒ 18 (100%) and 13 of 18 (72.2%; p > 0.05), respectively;Β detection of only partially visible masses β 15 of 18 (83.3%)Β and 17 of 18 (94.4%; p > 0.05); detection of lesions poorlyΒ visible or invisible on standard mammography imagesΒ due to the high density background (C-D types according toΒ the ACR 2013 classification) β 9 of 16 (56.3%) and 7 of 16Β (70.0%; p = 0.046). Total detection rate was 88.76% (316 ofΒ 356 cases) β for CAD I and 90.73% (323 of 356 cases;Β Ρ > 0.05) β for CAD II. Mean false positive marks rate wasΒ 1.8 and 1.3 per image, respectively, β for ACR Π-Π imagesΒ and 2.6 and 1.8 per image, respectively β for ACR C-DΒ images (p < 0.05).Conclusion. Generally the diagnostic value of CAD II isΒ not inferior that of CAD I in all analyzed situations, except theΒ poorly visible or invisible lesions on the dense breast background.Β Moreover, CAD II is probably superior CAD I in theΒ detection of spiculated small masses. The rate of falseΒ positive marks was significantly higher for CAD I.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠ°Π±ΠΎΡΡ ΡΠΈΡΡΠ΅ΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° (CAD)Β I ΠΈ II ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π½Π° ΠΎΠ±ΡΠΈΡΠ½ΠΎΠΉΒ Π±Π°Π·Π΅ Π½Π΅ΠΎΡΠΎΠ±ΡΠ°Π½Π½ΡΡ
ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ,Β ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΡΠΈΠ½Π½ΠΎΠΉ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ±Π΅ ΡΠΈΡΡΠ΅ΠΌΡ Π±ΡΠ»ΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½Ρ Π½Π° Π½Π°Π±ΠΎΡΠ΅ ΠΈΠ· 1532 ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΠΌΠΌ 356 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠΊΒ Ρ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΡΠ°ΠΊΠΎΠΌ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ (Π ΠΠ)Β Π½Π° ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΠ²Π°ΡΡ ΠΏΠΎΠ΄ΠΎΠ·ΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈΒ Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ Π½Π° ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ. Π Π°Π·ΠΌΠ΅Ρ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°Π²ΡΠΈΡ
Π ΠΠ, Π²Π°ΡΡΠΈΡΠΎΠ²Π°Π» ΠΎΡ 4 Π΄ΠΎ 35 ΠΌΠΌ (ΡΡΠ΅Π΄Π½ΠΈΠΉ β 13,4 Β± 6,3 ΠΌΠΌ). ΠΡΠΊΠ»ΡΡΠ°Π»ΠΈ ΡΠ»ΡΡΠ°ΠΈ Π ΠΠ, ΠΏΡΠΎΡΠ²Π»ΡΠ²ΡΠΈΠ΅ΡΡ ΡΠΎΠ»ΡΠΊΠΎ Π² Π²ΠΈΠ΄Π΅ ΡΠΊΠΎΠΏΠ»Π΅Π½ΠΈΠΉ ΠΌΠΈΠΊΡΠΎΠΊΠ°Π»ΡΡΠΈΠ½Π°ΡΠΎΠ²,Β ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ Π΄Π°Π½Π½Π°Ρ Π·Π°Π΄Π°ΡΠ° ΡΠ΅ΡΠ°Π΅ΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌΒ ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π±Π»ΠΎΠΊΠ°.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠΈΡΡΠ΅ΠΌ I ΠΈ II ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π±ΡΠ»ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ: ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΠΌΠ°Π»ΡΡ
ΡΠ°ΠΊΠΎΠ² (Π΄ΠΎ 10 ΠΌΠΌ) Ρ ΠΎΡΠ°Π³ΠΎΠ²ΡΠΌΒ ΡΠΎΡΡΠΎΠΌ β 41 (78,85%) ΠΈΠ· 52 ΠΈ 48 (92,31%; p > 0,05) ΠΈΠ· 52;Β ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π ΠΠ, ΠΏΡΠΎΡΠ²Π»ΡΡΡΠ΅Π³ΠΎΡΡ Π² Π²ΠΈΠ΄Π΅ Π°ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΠΈ, β 18 (100%) ΠΈΠ· 18 ΠΈ 13 (72,2%; p > 0,05) ΠΈΠ· 18; ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΡΠ°ΡΡΠΈΡΠ½ΠΎ ΡΡΠ΅Π·Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ β 15 (83,3%)Β ΠΈΠ· 18 ΠΈ 17 (94,4%; p > 0,05) ΠΈΠ· 18; ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΏΠ»ΠΎΡ
ΠΎ Π²ΠΈΠ΄ΠΈΠΌΡΡ
ΠΈΠ»ΠΈ Π²ΠΎΠΎΠ±ΡΠ΅ Π½Π΅Π²ΠΈΠ΄ΠΈΠΌΡΡ
Π½Π° ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
Π²Π²ΠΈΠ΄Ρ ΠΏΠ»ΠΎΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠ΅Π½Ρ
ΠΈΠΌΡ ΠΠΒ (ΡΠΈΠΏΡ C-D ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ACR 2013), β 9 (56,3%) ΠΈΠ· 16 ΠΈΒ 7 (70,0%; p = 0,046) ΠΈΠ· 16. ΠΠ±ΡΠ°Ρ ΡΠ°ΡΡΠΎΡΠ° ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡΒ ΠΏΠΎΠ΄ΠΎΠ·ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 88,76% (316 ΠΈΠ·Β 356 ΡΠ»ΡΡΠ°Π΅Π²) β Π΄Π»Ρ CAD I ΠΈ 90,73% (323 ΠΈΠ· 356 ΡΠ»ΡΡΠ°Π΅Π²;Β Ρ>0,05) β Π΄Π»Ρ CAD II. Π§Π°ΡΡΠΎΡΠ° Π»ΠΎΠΆΠ½ΠΎΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΡΡ
Β ΠΌΠ΅ΡΠΎΠΊ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° Π² ΡΡΠ΅Π΄Π½Π΅ΠΌ 1,8 ΠΈ 1,3 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ Π½Π°Β ΠΌΠ°ΠΌΠΌΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΡΠΈ ΡΠΈΠΏΠ°Ρ
ACR ΠβΠ ΠΈ 2,6 ΠΈ 1,8 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ β ΠΏΡΠΈ ΡΠΈΠΏΠ°Ρ
ACR CβD (p < 0,05).ΠΡΠ²ΠΎΠ΄Ρ. ΠΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ CAD II ΡΡΠ°Π²Π½ΠΈΠΌΠ° Ρ ΡΠ°ΠΊΠΎΠ²ΠΎΠΉΒ CAD I Π²ΠΎ Π²ΡΠ΅Ρ
ΡΠΈΡΡΠ°ΡΠΈΡΡ
, Π·Π° ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΌ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΏΠ»ΠΎΡ
ΠΎΒ Π²ΠΈΠ΄ΠΈΠΌΡΡ
ΠΈ Π½Π΅Π²ΠΈΠ΄ΠΈΠΌΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ Π²ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ ΠΏΠ»ΠΎΡΠ½ΠΎΠΉΒ ΠΏΠ°ΡΠ΅Π½Ρ
ΠΈΠΌΡ ΠΠ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, CAD II, Π²Π΅ΡΠΎΡΡΠ½ΠΎ, ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΠΈΡ CAD I Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΡΠΏΠΈΠΊΡΠ»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉΒ ΠΌΠ°Π»ΡΡ
ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ². Π§Π°ΡΡΠΎΡΠ° Π»ΠΎΠΆΠ½ΠΎΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠΊ ΠΏΡΠΈΒ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ CAD I Π±ΡΠ»Π° Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ Π²ΡΡΠ΅
Grayscale Color Mapping with the Mathematical Analysis of an Ultrasound Image in the Differential Diagnosis of Cystic and Solid Breast Masses
Objective. 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