Prognosis method to predict small-sized breast cancer affected by fibrocystic disease

The purpose of the study is to develop an effective radiological symptom-complex of small-sized breast cancer affected by fibrocystic breast disease by using multivariate statistical methods.Materials and methods. Radiological findings of small-sized breast cancer affected by fibrocystic mastopathy...

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Main Authors: S. A. Velichko, E. M. Slonimskaya, I. G. Frolova, D. G. Bukharin, A. V. Doroshenko
Format: Article
Language:English
Published: Siberian State Medical University (Tomsk) 2017-04-01
Series:Бюллетень сибирской медицины
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Online Access:https://bulletin.ssmu.ru/jour/article/view/769
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author S. A. Velichko
E. M. Slonimskaya
I. G. Frolova
D. G. Bukharin
A. V. Doroshenko
author_facet S. A. Velichko
E. M. Slonimskaya
I. G. Frolova
D. G. Bukharin
A. V. Doroshenko
author_sort S. A. Velichko
collection DOAJ
description The purpose of the study is to develop an effective radiological symptom-complex of small-sized breast cancer affected by fibrocystic breast disease by using multivariate statistical methods.Materials and methods. Radiological findings of small-sized breast cancer affected by fibrocystic mastopathy were analyzed in 100 patients with histologically verified diagnosis.Results. It was revealed that the conventional approach to the analysis of mammograms based on the detection of the primary, secondary and indirect mammographic signs of small-sized breast cancer is not effective enough - the sensitivity of mammography is only 62%. Fibrocystic disease and moderate-to-severe sclerosing adenosis make small-sized breast cancer hard to visualize by mammography. The detailed analysis of mammograms allowed us to identify the additional manifestations of small-sized breast cancer affected by mastopathy. The computer program allowing us to evaluate the risk of small-size breast cancer and the diagnostic algorithm for detecting small size breast cancer with sensitivity of 92% were developed.
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institution DOAJ
issn 1682-0363
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language English
publishDate 2017-04-01
publisher Siberian State Medical University (Tomsk)
record_format Article
series Бюллетень сибирской медицины
spelling doaj-art-53b39dffb13b47ecbbe43c1a0a61980f2025-08-20T03:21:42ZengSiberian State Medical University (Tomsk)Бюллетень сибирской медицины1682-03631819-36842017-04-01161131910.20538/1682-0363-2017-1-13-19617Prognosis method to predict small-sized breast cancer affected by fibrocystic diseaseS. A. Velichko0E. M. Slonimskaya1I. G. Frolova2D. G. Bukharin3A. V. Doroshenko4Tomsk Cancer Research Institute, Tomsk National Research Medical Center (TNRMC), Russian Academy of Sciences (RAS)Tomsk Cancer Research Institute, Tomsk National Research Medical Center (TNRMC), Russian Academy of Sciences (RAS)Tomsk Cancer Research Institute, Tomsk National Research Medical Center (TNRMC), Russian Academy of Sciences (RAS)Tomsk Cancer Research Institute, Tomsk National Research Medical Center (TNRMC), Russian Academy of Sciences (RAS)Tomsk Cancer Research Institute, Tomsk National Research Medical Center (TNRMC), Russian Academy of Sciences (RAS)The purpose of the study is to develop an effective radiological symptom-complex of small-sized breast cancer affected by fibrocystic breast disease by using multivariate statistical methods.Materials and methods. Radiological findings of small-sized breast cancer affected by fibrocystic mastopathy were analyzed in 100 patients with histologically verified diagnosis.Results. It was revealed that the conventional approach to the analysis of mammograms based on the detection of the primary, secondary and indirect mammographic signs of small-sized breast cancer is not effective enough - the sensitivity of mammography is only 62%. Fibrocystic disease and moderate-to-severe sclerosing adenosis make small-sized breast cancer hard to visualize by mammography. The detailed analysis of mammograms allowed us to identify the additional manifestations of small-sized breast cancer affected by mastopathy. The computer program allowing us to evaluate the risk of small-size breast cancer and the diagnostic algorithm for detecting small size breast cancer with sensitivity of 92% were developed.https://bulletin.ssmu.ru/jour/article/view/769breast cancermammographymammographic signs of small size breast cancerfibrocystic disease
spellingShingle S. A. Velichko
E. M. Slonimskaya
I. G. Frolova
D. G. Bukharin
A. V. Doroshenko
Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
Бюллетень сибирской медицины
breast cancer
mammography
mammographic signs of small size breast cancer
fibrocystic disease
title Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
title_full Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
title_fullStr Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
title_full_unstemmed Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
title_short Prognosis method to predict small-sized breast cancer affected by fibrocystic disease
title_sort prognosis method to predict small sized breast cancer affected by fibrocystic disease
topic breast cancer
mammography
mammographic signs of small size breast cancer
fibrocystic disease
url https://bulletin.ssmu.ru/jour/article/view/769
work_keys_str_mv AT savelichko prognosismethodtopredictsmallsizedbreastcanceraffectedbyfibrocysticdisease
AT emslonimskaya prognosismethodtopredictsmallsizedbreastcanceraffectedbyfibrocysticdisease
AT igfrolova prognosismethodtopredictsmallsizedbreastcanceraffectedbyfibrocysticdisease
AT dgbukharin prognosismethodtopredictsmallsizedbreastcanceraffectedbyfibrocysticdisease
AT avdoroshenko prognosismethodtopredictsmallsizedbreastcanceraffectedbyfibrocysticdisease