Reducing Unnecessary Biopsy and Follow-up of Benign Cystic Breast Lesions

Berg, Wendie A.

doi:10.1148/radiol.2020200037

Cited by 18 publications

(10 citation statements)

References 11 publications

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“…Кісти грудної залози (КГЗ) становлять перманентну клінічну проблему, оскільки їх регулярно діагностують у кожної другої пацієнтки зі скаргами з боку грудної залози (ГЗ) або випадково, з частотою до 20-25%, виявляють у безсимптомних жінок репродуктивного віку [1]. При обмеженості [2] або надмірній деталізації [3] відомих класифікацій найбільш практичним вважають ехографічний розподіл КГЗ на прості, ускладнені та комплексні, що дає змогу уникнути непотрібних біопсій і операцій [4].…”

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Diagnosis of complex breast cysts using X-ray pneumocystography and endoscopic mammocystoscopy

Аксьонов

2022

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Background. Complex cysts of the breast (CCB), in contrast to simple and complicated cysts, are characterized by a high (up to 31.0%) oncological potential. Mammography and ultrasound (US) as the most common methods of radiological diagnosis do not allow to classify CCB on benign and malignant, and in the case of breast cancer (BC) are not detected the establishment of its molecular genetic subtype.Objectives: to analyze and popularize own experience of using endoscopic mammocystoscopy (EMCS) and X-ray pneumocystography (RPCG) for the diagnosis of CCB and biopsy navigation. Materials and methods. The clinical and anamnestic data, the results of radiological, endoscopic and pathomorphological studies in 286 adult women with simple, complicated breast cysts and CCB were analyzed (n = 45, 134 and 142, respectively).Results. The diagnostic and navigation capabilities of US are limited by frequent (18.1%) false-negative results, which are caused by atypical echosemiotics of CCB. Trepan-biopsy under US control becomes impossible when visualization of the CCB disappears, which often occurs during fine-needle aspiration of the liquid component. Technical difficulties arise when imitating a puncture needle with linear hyperechoic structures induced by Cooper’s ligaments, as well as when positioning the CCB in a bulky, non-fixed gland. In terms of diagnostic specificity and prognostic significance of a negative result, RPCG is slightly (5.6 and 4.7%, respectively) inferior to US and in certain clinical situations it can be used to assess the likelihood of BC, and in case of positive results for navigate a stereotaxic сore needle biopsy. EMCS is a low-traumatic procedure that provides direct visual contact with intracystic neoplasms, allows assessing the probability of BC and conducting a pinch biopsy.Conclusions. The consistent combined use of well-known and author’s methods with the involvement of US, RPCG and EMCS according to the given algorithm ensures effective management of CCB at the modern level.

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Diagnosis of complex breast cysts using X-ray pneumocystography and endoscopic mammocystoscopy

Аксьонов

2022

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“…When mammography detects a suspicious breast tumor, breast biopsy is generally performed [5]. Although breast biopsies provide a definite diagnosis, the statistics show that most biopsies are benign [6,7]. Thus, many studies have been carried out for efficient early diagnosis and to minimize the unnecessary biopsy rates in the literature.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Akkur¹,

TURK²,

Eroğul³

2023

Computer Systems Science and Engineering

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Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

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“…Early and effective diagnosis has always been the challenge of breast radiology, aimed at reducing mortality and offering better management of those patients. The main goal in breast imaging is the early detection of neoplasms and avoidance of unnecessary biopsies [ 2 , 3 ]. Contrast-enhanced spectral mammography (CESM) seems to be a promising method in this regard [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography

Nicosia

Bozzini

Palma

et al. 2022

Cancers

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Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years’ experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22–83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

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Reducing Unnecessary Biopsy and Follow-up of Benign Cystic Breast Lesions

Cited by 18 publications

References 11 publications

Diagnosis of complex breast cysts using X-ray pneumocystography and endoscopic mammocystoscopy

Diagnosis of complex breast cysts using X-ray pneumocystography and endoscopic mammocystoscopy

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography

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