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1.
BMC Med Imaging ; 22(1): 166, 2022 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-36104679

RESUMO

OBJECTIVE: This study is aimed to explore the value of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer (TNBC). MATERIALS AND METHODS: Initially, the clinical and X-ray data of patients (n = 319, age of 54 ± 14) with breast cancer (triple-negative-65, non-triple-negative-254) from the First Affiliated Hospital of Soochow University (n = 211, as a training set) and Suzhou Municipal Hospital (n = 108, as a verification set) from January 2018 to February 2021 are retrospectively analyzed. Comparing the mediolateral oblique (MLO) and cranial cauda (CC) mammography images, the mammography images with larger lesion areas are selected, and the image segmentation and radiomics feature extraction are then performed by the MaZda software. Further, the Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) are used to select three sets of feature subsets. Moreover, the score of each patient's radiomics signature (Radscore) is calculated. Finally, the receiver operating characteristic curve (ROC) is analyzed to calculate the AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of TNBC. RESULTS: A significant difference in the mammography manifestation between the triple-negative and the non-triple-negative groups (P < 0.001) is observed. The (POE + ACC)-NDA method showed the highest accuracy of 88.39%. The Radscore of triple-negative and non-triple-negative groups in the training set includes - 0.678 (- 1.292, 0.088) and - 2.536 (- 3.496, - 1.324), respectively, with a statistically significant difference (Z = - 6.314, P < 0.001). In contrast, the Radscore in the validation set includes - 0.750 (- 1.332, - 0.054) and - 2.223 (- 2.963, - 1.256), with a statistically significant difference (Z = - 4.669, P < 0.001). In the training set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC include 0.821 (95% confidence interval 0.752-0.890), 74.4%, 82.5%, 72.5%, 41.2%, and 94.6%, respectively. In the validation set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC are of 0.809 (95% confidence interval 0.711-0.907), 80.6%, 72.0%, 80.7%, 55.5%, and 93.1%, respectively. CONCLUSION: In summary, we firmly believe that this mammography-based radiomics signature could be useful in the preoperative prediction of TNBC due to its high value.


Assuntos
Neoplasias de Mama Triplo Negativas , Humanos , Mamografia/métodos , Valor Preditivo dos Testes , Curva ROC , Estudos Retrospectivos , Neoplasias de Mama Triplo Negativas/diagnóstico por imagem
2.
Comput Biol Med ; 149: 106073, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-36103745

RESUMO

Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC throughout their lifetime. Early detection of this life-threatening disease not only increases the survival rate but also reduces the treatment cost. Fortunately, advancements in radiographic imaging like "Mammograms", "Computed Tomography (CT)", "Magnetic Resonance Imaging (MRI)", "3D Mammography", and "Histopathological Imaging (HI)" have made it feasible to diagnose this life-taking disease at an early stage. However, the analysis of radiographic images and Histopathological images is done by experienced radiologists and pathologists, respectively. The process is not only costly but also error-prone. Over the last ten years, Computer Vision and Machine Learning (ML) have transformed the world in every way possible. Deep learning (DL), a subfield of ML has shown outstanding results in a variety of fields, particularly in the biomedical industry, because of its ability to handle large amounts of data. DL techniques automatically extract the features by analyzing the high dimensional and correlated data efficiently. The potential and ability of DL models have also been utilized and evaluated in the identification and prognosis of BC, utilizing radiographic and Histopathological images, and have performed admirably. However, AI has shown good claims in retrospective studies only. External validations are needed for translating these cutting-edge AI tools as a clinical decision maker. The main aim of this research work is to present the critical analysis of the research and findings already done to detect and classify BC using various imaging modalities including "Mammography", "Histopathology", "Ultrasound", "PET/CT", "MRI", and "Thermography". At first, a detailed review of the past research papers using Machine Learning, Deep Learning and Deep Reinforcement Learning for BC classification and detection is carried out. We also review the publicly available datasets for the above-mentioned imaging modalities to make future research more accessible. Finally, a critical discussion section has been included to elaborate open research difficulties and prospects for future study in this emerging area, demonstrating the limitations of Deep Learning approaches.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Aprendizado de Máquina , Mamografia/métodos , Estudos Retrospectivos
3.
Croat Med J ; 63(4): 326-334, 2022 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-36046929

RESUMO

AIM: To assess the uptake of the Croatian National Breast Cancer Screening Program from 2006 to 2016. METHODS: The Croatian National Breast Cancer Screening Program, a biennial program targeting women aged 50-69, started in October 2006. From 2006 to 2016, four cycles were completed. One cycle lasted two years, with the exception of the first cycle, which lasted three years. To determine the number of detected cancers in each cycle, the screening program data were merged with the data of the Croatian National Cancer Registry. Our results were compared with the reference values from the European guidelines for quality assurance in breast cancer screening and diagnosis. RESULTS: Around 150 000 mammography exams were performed every year. The response rates for cycle 1, cycle 2, cycle 3, and cycle 4 were 63%, 57%, 60%, and 59%, respectively. Further assessment rate was 6.5%. Breast cancer was identified in 5583 women, with 4.8 cancers detected per 1000 mammography exams. CONCLUSION: The National Breast Cancer Screening Program in Croatia reached a substantial proportion of the target group. Yet, additional efforts are needed to reach at least 70% of the target population.


Assuntos
Neoplasias da Mama , Detecção Precoce de Câncer , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Croácia/epidemiologia , Detecção Precoce de Câncer/métodos , Feminino , Humanos , Mamografia , Programas de Rastreamento
4.
Ann Acad Med Singap ; 51(8): 493-501, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-36047524

RESUMO

INTRODUCTION: Breast cancer is a leading cause of cancer death among women, and its age-standardised incidence rate is one of the highest in Asia. We aimed to review studies on barriers to breast cancer screening to inform future policies in Singapore. METHOD: This was a literature review of both quantitative and qualitative studies published between 2012 and 2020 using PubMed, Google Scholar and Cochrane databases, which analysed the perceptions and behaviours of women towards breast cancer screening in Singapore. RESULTS: Through a thematic analysis based on the Health Belief Model, significant themes associated with low breast cancer screening uptake in Singapore were identified. The themes are: (1) high perceived barriers versus benefits, including fear of the breast cancer screening procedure and its possible outcomes, (2) personal challenges that impede screening attendance and paying for screening and treatment, and (3) low perceived susceptibility to breast cancer. CONCLUSION: Perceived costs/barriers vs benefits of screening appear to be the most common barriers to breast cancer screening in Singapore. Based on the barriers identified, increasing convenience to get screened, reducing mammogram and treatment costs, and improving engagement with support groups are recommended to improve the screening uptake rate in Singapore.


Assuntos
Neoplasias da Mama , Detecção Precoce de Câncer , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/epidemiologia , Feminino , Conhecimentos, Atitudes e Prática em Saúde , Humanos , Mamografia , Programas de Rastreamento , Singapura/epidemiologia
5.
Oncol Nurs Forum ; 49(5): 471-479, 2022 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-36067239

RESUMO

OBJECTIVES: To determine the feasibility and acceptability of using a decision aid (DA) in a breast surgery clinic. SAMPLE & SETTING: 42 patients with minimally suspicious mammograms and two physicians participated in this study at an outpatient breast specialty clinic in Virginia. METHODS & VARIABLES: A quasiexperimental single group pilot study was conducted to determine the feasibility of DecisionKEYS, a theory-based, interactive DA intervention. Patients with minimally suspicious mammogram results chose between breast biopsy or close imaging follow-up. The Decisional Conflict Scale was used to measure decisional conflict. The Decision-Making Quality Scale was used to evaluate the overall decision process. Postintervention physician and patient feedback evaluated feasibility and acceptability. RESULTS: Participants and physicians rated the DA as helpful. Decisional Conflict Scale scores were low before and after the intervention. Physicians reported the DA was feasible for workflow, and the majority reported using the DA in making final recommendations. Management recommendation (breast biopsy, close imaging follow-up) changed in 26 of 42 cases from pre- to postintervention. The majority of participants underwent breast biopsy. IMPLICATIONS FOR NURSING: The feasibility and acceptability of the DA were beneficial to patients and clinic workflow.


Assuntos
Técnicas de Apoio para a Decisão , Detecção Precoce de Câncer , Monofosfato de Adenosina , Tomada de Decisões , Humanos , Mamografia , Projetos Piloto
6.
Technol Cancer Res Treat ; 21: 15330338221104567, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36071652

RESUMO

Objectives: Iterative (eg, simultaneous algebraic reconstruction technique [SART]) and analytical (eg, filtered back projection [FBP]) image reconstruction techniques have been suggested to provide adequate three-dimensional (3D) images of the breast for capturing microcalcifications in digital breast tomosynthesis (DBT). To decide on the reconstruction method in clinical DBT, it must first be tested in a simulation resembling the real clinical environment. The purpose of this study is to introduce a 3D realistic breast phantom for determining the reconstruction method in clinical applications. Methods: We designed a 3D realistic breast phantom with varying dimensions (643-5123) mimicking some structures of a real breast such as milk ducts, lobules, and ribs using TomoPhantom software. We generated microcalcifications, which mimic cancerous cells, with a separate MATLAB code and embedded them into the phantom for testing and benchmark studies in DBT. To validate the characterization of the phantom, we tested the distinguishability of microcalcifications by performing 3D image reconstruction methods (SART and FBP) using Laboratory of Computer Vision (LAVI) open-source reconstruction toolbox. Results: The creation times of the proposed realistic breast phantom were seconds of 2.5916, 8.4626, 57.6858, and 472.1734 for 643, 1283, 2563, and 5123, respectively. We presented reconstructed images and quantitative results of the phantom for SART (1-2-4-8 iterations) and FBP, with 11 to 23 projections. We determined qualitatively and quantitatively that SART (2-4 iter.) yields better results than FBP. For example, for 23 projections, the contrast-to-noise ratio (CNR) values of SART (2 iter.) and FBP were 2.871 and 0.497, respectively. Conclusions: We created a computationally efficient realistic breast phantom that is eligible for reconstruction and includes anatomical structures and microcalcifications, successfully. By proposing this breast phantom, we provided the opportunity to test which reconstruction methods can be used in clinical applications vary according to various parameters such as the No. of iterations and projections in DBT.


Assuntos
Algoritmos , Calcinose , Mama/diagnóstico por imagem , Calcinose/diagnóstico por imagem , Humanos , Imageamento Tridimensional/métodos , Mamografia/métodos
7.
Int J Mol Sci ; 23(17)2022 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-36077324

RESUMO

For many cancer types, being undetectable from early symptoms or blood tests, or often detected at late stages, medical imaging emerges as the most efficient tool for cancer screening. MRI, ultrasound, X-rays (mammography), and X-ray CT (CT) are currently used in hospitals with variable costs. Diagnostic materials that can detect breast tumors through molecular recognition and amplify the signal at the targeting site in combination with state-of-the-art CT techniques, such as dual-energy CT, could lead to a more precise detection and assist significantly in image-guided intervention. Herein, we have developed a ligand-specific X-ray contrast agent that recognizes α5ß1 integrins overexpressed in MDA-MB-231 breast cancer cells for detection of triple (-) cancer, which proliferates very aggressively. In vitro studies show binding and internalization of our nanoprobes within those cells, towards uncoated nanoparticles (NPs) and saline. In vivo studies show high retention of ~3 nm ligand-PEG-S-AuNPs in breast tumors in mice (up to 21 days) and pronounced CT detection, with statistical significance from saline and iohexol, though only 0.5 mg of metal were utilized. In addition, accumulation of ligand-specific NPs is shown in tumors with minimal presence in other organs, relative to controls. The prolonged, low-metal, NP-enhanced spectral-CT detection of triple (-) breast cancer could lead to breakthrough advances in X-ray cancer diagnostics, nanotechnology, and medicine.


Assuntos
Nanopartículas Metálicas , Neoplasias , Animais , Meios de Contraste/química , Ouro/química , Ligantes , Mamografia/métodos , Nanopartículas Metálicas/química , Camundongos , Tomografia Computadorizada por Raios X/métodos
8.
Korean J Radiol ; 23(9): 866-877, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-36047541

RESUMO

OBJECTIVE: The optimal imaging approach for evaluating pathological nipple discharge remains unclear. We investigated the value of adding ductography to ultrasound (US) for evaluating pathologic nipple discharge in patients with negative mammography findings. MATERIALS AND METHODS: From July 2003 to December 2018, 101 women (mean age, 46.3 ± 12.2 years; range, 23-75 years) with pathologic nipple discharge were evaluated using pre-ductography (initial) US, ductography, and post-ductography US. The imaging findings were reviewed retrospectively. The standard reference was surgery (70 patients) or > 2 years of follow-up with US (31 patients). The diagnostic performances of initial US, ductography, and post-ductography US for detecting malignancy were compared using the McNemar's test or a generalized estimating equation. RESULTS: In total, 47 papillomas, 30 other benign lesions, seven high-risk lesions, and 17 malignant lesions were identified as underlying causes of pathologic nipple discharge. Only eight of the 17 malignancies were detected on the initial US, while the remaining nine malignancies were detected by ductography. Among the nine malignancies detected by ductography, eight were detected on post-ductography US and could be localized for US-guided intervention. The sensitivities of ductography (94.1% [16/17]) and post-ductography US (94.1% [16/17]) were significantly higher than those of initial US (47.1% [8/17]; p = 0.027 and 0.013, respectively). The negative predictive value of post-ductography US (96.9% [31/32]) was significantly higher than that of the initial US (83.3% [45/54]; p = 0.006). Specificity was significantly higher for initial US than for ductography and post-ductography US (p = 0.001 for all). CONCLUSION: The combined use of ductography and US has a high sensitivity for detecting malignancy in patients with pathologic nipple discharge and negative mammography. Ductography findings enable lesion localization on second-look post-ductography US, thus facilitating the selection of optimal treatment plans.


Assuntos
Neoplasias da Mama , Derrame Papilar , Adulto , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Mamografia/métodos , Pessoa de Meia-Idade , Derrame Papilar/diagnóstico por imagem , Mamilos/diagnóstico por imagem , Mamilos/patologia , Estudos Retrospectivos , Ultrassonografia , Ultrassonografia Mamária
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 1667-1670, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085665

RESUMO

Breast cancer remains the leading cause of cancer deaths and the second highest cause of death, in general, among women worldwide. Fortunately, over the last few decades, with the introduction of mammography, the mortality rate of breast cancer has significantly decreased. However, accurate classification of breast masses in mammograms is especially challenging. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. In this study, classification of benign and malignant masses, based on the subtraction of temporally sequential digital mammograms and machine learning, is proposed. The performance of the algorithm was evaluated on a dataset created for the purposes of this study. In total, 196 images from 49 patients, with precisely annotated mass locations and biopsy confirmed malignant cases, were included. Ninety-six features were extracted and five feature selection algorithms were employed to identify the most important features. Ten classifiers were tested using leave-one-patient-out and 7-fold cross-validation. Neural Networks, achieved the highest classification performance with 90.85% accuracy and 0.91 AUC, an improvement compared to the state-of-the-art. These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the classification of breast masses as benign or malignant.


Assuntos
Neoplasias da Mama , Mamografia , Algoritmos , Neoplasias da Mama/diagnóstico por imagem , Diagnóstico por Computador , Feminino , Humanos , Redes Neurais de Computação
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 1002-1007, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085669

RESUMO

Breast Cancer has been the primary reason for mortality in women of age between twenties and sixties worldwide; moreover early detection and treatment provides patients to get absolute treatment and decrease the mortality rate. Furthermore, recent research indicates that most experienced physicians have plenty of limitations, hence the plethora of work has been carried out to develop an automated mechanism of segmentation and classification of affected area and type of cancer; however, it is still considered to be highly challenging due to the variability of tumor in shape, low signal to noise ratio, shape, size and location of tumor. Furthermore, mammographic mass segmentation and detection are performed as a separate task and a convolution neural network is a highly adopted architecture for the same. In this research, we have designed and developed unified CNN architecture to perform the segmentation and detection of a breast mass. The unified-CNN architecture comprises a novel module for convolution which is combined through additional offset. Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is implied to achieve the high prediction. Furthermore, unified-CNN is evaluated using the metrics like true positive Rate at FPI (False Positive per Image) and Dice Index on INBreast dataset, also comparative analysis is out carried with various existing methodology. Unified-CNN is developed through improvising CNN. It introduces a novel module at the convolution layer to aim for a high-level feature map in order to get a high prediction. RRS (Random Region Selection) algorithm is used as the data augmentation approach to select the boundary region of the affected area; further robust model training is designed and optimized for process to make optimal. Unified-CNN introduces novel module at the convolution layer to aim for high level feature map in order to get high prediction; further ROI pooling is utilized for boundary detection in images.


Assuntos
Neoplasias da Mama , Mamografia , Algoritmos , Benchmarking , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Redes Neurais de Computação
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 865-868, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085709

RESUMO

One in every eight women will get breast cancer during their lifetime. Therefore, the early diagnosis of the lesions is fundamental to improve the chances of recovery. To find breast cancers, breast screening using techniques such as mammography and ultrasound (US) imaging scans are often used. When a lesion is found, a breast biopsy is performed to extract a tissue sample for analysis. The breast biopsy is usually assisted by an US to help find the lesion and guide the needle to its location. However, the identification of the needle tip in US image is challenging, possibly resulting in puncture failures. In this paper, we intend to study the potential of a sensorized needle guide system that provides information about the needle angle and displacement in respect to the US probe. Laboratory tests were initially conducted to evaluate the accuracy of each sensor in controlled conditions. After, a practical experiment with the US probe, working as a proof of concept, was performed. The angle sensor showed a root mean square error (RMSE) of 0.48 degrees and the displacement sensor showed a RMSE of 0.26mm after being calibrated. For the US probe tests, the displacement sensor shows high errors in the range of 1.19mm to 2.05mm due to mechanical reasons. Overall, the proposed system showed its potential to be used to accurately estimate needle tip localization throughout breast biopsies guided by US, corroborating its potential clinical application. Clinical relevance - Potential for clinical application where precise needle localization in ultrasound image is required.


Assuntos
Neoplasias da Mama , Ultrassonografia Mamária , Biópsia , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia , Ultrassonografia
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 508-511, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085729

RESUMO

Breast cancer is one of the most diagnosed forms of cancer among women worldwide. However, the survival rate is very high when the tumor is diagnosed early. The search for diagnostic techniques increasingly able to detect lesions of the order of a few millimeters and to overcome the limitations of current diagnostic techniques (e.g., the X-ray mammography, currently used as standard for screening campaigns) is always active. Among the main emerging techniques, microwave and millimeter-wave imaging systems have been proposed, using either radar or tomographic approaches. In this paper, a novel dual-step millimeter-wave imaging which combines the advantages of tomographic and radar approaches is proposed. The goal of this work is to reconstruct the dielectric profile of suspicious regions by exploiting the morphological information from the radar maps as a priori information within quantitative tomographic techniques. Promising preliminary dielectric reconstruction results against simulated data are shown in both single- and dual-target scenarios, in which high-density healthy and tumor tissues are present. The reconstruction results were compared to the dielectric characteristics of human breast exvivo tissues used in the simulated models. The proposed dual-step approach allows to distinguish the nature of the targets also in the most challenging case represented by the co-presence of high-density healthy tissues and a malignant lesion, thus paving the way for a deeper investigation of this approach in experimental scenarios. Clinical Relevance-The proposed dual-step approach in the millimeter-wave regime allows to improve the reliability of the diagnostic technique, increasing its specificity.


Assuntos
Neoplasias da Mama , Radar , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia , Reprodutibilidade dos Testes , Tomografia Computadorizada por Raios X
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 2144-2148, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085843

RESUMO

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammogra-phy (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Benchmarking , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 1440-1443, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36086431

RESUMO

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the mammogram images. CNN models were recently shown to improve the risk discrimination in full-field mammograms. This study aims to improve risk prediction models using bilateral analysis. Bilateral analysis is the process of comparing two breasts to verify presence of anomalies. We developed a Siamese neural network to leverage the bilateral information and asymmetries between the two mammograms of the same patient. We tested our model on 271 patients and compared the results of our Siamese model against the traditional unilateral CNN model. Our results showed AUCs of 0.75 and 0.70 respectively (p = 0.0056). The Siamese model also exhibits higher sensitivity, specificity, precision, and false positive rate with values of 0.68, 0.69, 0.71, 0.31 respectively. While the CNN values were 0.61, 0.66, 0.67, 0.34 respectively. We merged both models by two techniques using pre-trained weights and weighted voting ensemble. The merging technique boosted the AUC to 0.78. The results suggest that bilateral analysis can significantly improve the risk discrimination.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia/métodos , Redes Neurais de Computação
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3526-3529, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36086472

RESUMO

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due to: i) the low contrast of the lesion boundaries; ii) the extremely variable lesions' sizes and shapes; and iii) some extremely small lesions on the mammogram image. To overcome these drawbacks, Deep Learning methods have been implemented and have shown impressive results when applied to medical image segmentation. This work presents a benchmark for breast lesion segmentation in mammography images, where six state-of-the-art methods were evaluated on 1692 mammograms from a public dataset (CBIS-DDSM), and compared considering the following six metrics: i) Dice coefficient; ii) Jaccard index; iii) accuracy; iv) recall; v) specificity; and vi) precision. The base U-Net, UNETR, DynUNet, SegResNetVAE, RF-Net, MDA-Net architectures were trained with a combination of the cross-entropy and Dice loss functions. Although the networks presented Dice scores superior to 86%, two of them managed to distinguish themselves. In general, the results demonstrate the efficiency of the MDA-Net and DynUnet with Dice scores of 90.25% and 89.67%, and accuracy of 93.48% and 93.03%, respectively. Clinical Relevance--- The presented comparative study allowed to identify the current performance of deep learning strategies on the segmentation of breast lesions.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia
16.
Breast ; 65: 180-186, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-36049384

RESUMO

BACKGROUND: This study investigated whether the association between family history of breast cancer in first-degree relatives and breast cancer risk varies by breast density. METHODS: Women aged 40 years and older who underwent screening between 2009 and 2010 were followed up until 2020. Family history was assessed using a self-reported questionnaire. Using Breast Imaging Reporting and Data System (BI-RADS), breast density was categorized into dense breast (heterogeneously or extremely dense) and non-dense breast (almost entirely fatty or scattered areas of fibro-glandular). Cox regression model was used to assess the association between family history and breast cancer risk. RESULTS: Of the 4,835,507 women, 79,153 (1.6%) reported having a family history of breast cancer and 77,238 women developed breast cancer. Family history led to an increase in the 5-year cumulative incidence in women with dense- and non-dense breasts. Results from the regression model with and without adjustment for breast density yielded similar HRs in all age groups, suggesting that breast density did not modify the association between family history and breast cancer. After adjusting for breast density and other factors, family history of breast cancer was associated with an increased risk of breast cancer in all three age groups (age 40-49 years: aHR 1.96, 95% confidence interval [CI] 1.85-2.08; age 50-64 years: aHR 1.70, 95% CI 1.58-1.82, and age ≥65 years: aHR 1.95, 95% CI 1.78-2.14). CONCLUSION: Family history of breast cancer and breast density are independently associated with breast cancer. Both factors should be carefully considered in future risk prediction models of breast cancer.


Assuntos
Densidade da Mama , Neoplasias da Mama , Adulto , Idoso , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/genética , Estudos de Coortes , Feminino , Humanos , Mamografia , Pessoa de Meia-Idade , República da Coreia/epidemiologia , Fatores de Risco
17.
JBI Evid Synth ; 20(9): 2370-2377, 2022 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-36065910

RESUMO

OBJECTIVE: The objective of this review is to map the global evidence on interventions aiming to enhance the patient experience during mammography examination. INTRODUCTION: Mammography is the examination of choice to detect breast cancer, which is the most common malignant condition among women globally. However, this examination can cause psychological distress, discomfort, and pain for patients. To limit these negative experiences, and to promote patient engagement in diagnostic and screening examinations, some interventions have been tested in clinical practice. Each intervention has key differing features that need to be explored in a scoping review. This mapping will help inform mammography professionals and researchers. INCLUSION CRITERIA: This review will consider studies that focus on women, men, transgender, nonbinary, or intersexual persons undergoing diagnostic or screening mammography. It will consider studies evaluating interventions and reporting data on the patient experience. These interventions may, for instance, be related to the information provided, breast compression, relaxation, medication, or physical environment. The review will also describe the outcomes related to patient experience (eg, anxiety, pain, discomfort). METHODS: The search strategy will aim to find published and unpublished studies and will be conducted in MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, and ProQuest Dissertation and Theses. Furthermore, three registries will be searched for ongoing studies. This review will be conducted following JBI methodology, utilizing the three-step search strategy with two independent reviewers performing study selection and data extraction. The results, frequencies, and conceptual categories will be presented in a tabular and narrative summary. SCOPING REVIEW REGISTRATION: Open Science Framework ( https://osf.io/fn865/ ).


Assuntos
Neoplasias da Mama , Mamografia , Neoplasias da Mama/diagnóstico por imagem , Detecção Precoce de Câncer , Feminino , Humanos , Masculino , Dor , Avaliação de Resultados da Assistência ao Paciente , Literatura de Revisão como Assunto
18.
Curr Oncol ; 29(8): 5508-5516, 2022 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-36005173

RESUMO

AIM: To compare digital breast tomosynthesis (DBT) and ultrasound in women recalled for assessment after a positive screening mammogram and assess the potential for each of these tools to reduce unnecessary biopsies. METHODS: This data linkage study included 538 women recalled for assessment from January 2017 to December 2019. The association between the recalled mammographic abnormalities and breast density was analysed using the chi-square independence test. Relative risks and the number of recalled cases requiring DBT and ultrasound assessment to prevent one unnecessary biopsy were compared using the McNemar test. RESULTS: Breast density significantly influenced recall decisions (p < 0.001). Ultrasound showed greater potential to decrease unnecessary biopsies than DBT: in entirely fatty (21% vs. 5%; p = 0.04); scattered fibroglandular (23% vs. 10%; p = 0.003); heterogeneously dense (34% vs. 7%; p < 0.001) and extremely dense (39% vs. 9%; p < 0.001) breasts. The number of benign cases needing assessment to prevent one unnecessary biopsy was significantly lower with ultrasound than DBT in heterogeneously dense (1.8 vs. 7; p < 0.001) and extremely dense (1.9 vs. 5.1; p = 0.03) breasts. CONCLUSION: Women with dense breasts are more likely to be recalled for assessment and have a false-positive biopsy. Women with dense breasts benefit more from ultrasound assessment than from DBT.


Assuntos
Neoplasias da Mama , Mamografia , Biópsia , Mama/diagnóstico por imagem , Mama/patologia , Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Detecção Precoce de Câncer , Feminino , Humanos
19.
Curr Oncol ; 29(8): 5627-5643, 2022 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-36005182

RESUMO

The relationship between Canadian mammography screening practices for women 40-49 and breast cancer (BC) stage at diagnosis in women 40-49 and 50-59 years was assessed using data from the Canadian Cancer Registry, provincial/territorial screening practices, and screening information from the Canadian Community Health Survey. For the 2010 to 2017 period, women aged 40-49 were diagnosed with lesser relative proportions of stage I BC (35.7 vs. 45.3%; p < 0.001), but greater proportions of stage II (42.6 vs. 36.7%, p < 0.001) and III (17.3 vs. 13.1%, p < 0.001) compared to women 50-59. Stage IV was lower among women 40-49 than 50-59 (4.4% vs. 4.8%, p = 0.005). Jurisdictions with organised screening programs for women 40-49 with annual recall (screeners) were compared with those without (comparators). Women aged 40-49 in comparator jurisdictions had higher proportions of stages II (43.7% vs. 40.7%, p < 0.001), III (18.3% vs. 15.6%, p < 0.001) and IV (4.6% vs. 3.9%, p = 0.001) compared to their peers in screener jurisdictions. Based on screening practices for women aged 40-49, women aged 50-59 had higher proportions of stages II (37.2% vs. 36.0%, p = 0.003) and III (13.6% vs. 12.3%, p < 0.001) in the comparator versus screener groups. The results of this study can be used to reassess the optimum lower age for BC screening in Canada.


Assuntos
Neoplasias da Mama , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/prevenção & controle , Canadá , Detecção Precoce de Câncer , Feminino , Humanos , Mamografia/métodos , Programas de Rastreamento
20.
Curr Oncol ; 29(8): 5644-5654, 2022 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-36005183

RESUMO

Quality medical practice is based on science and evidence. For over a half-century, the efficacy of breast cancer screening has been challenged, particularly for women aged 40-49. As each false claim has been raised, it has been addressed and refuted based on science and evidence. Nevertheless, misinformation continues to be promoted, resulting in confusion for women and their physicians. Early detection has been proven to save lives for women aged 40-74 in randomized controlled trials of mammography screening. Observational studies, failure analyses, and incidence of death studies have provided evidence that there is a major benefit when screening is introduced to the general population. In large part due to screening, there has been an over 40% decline in deaths from breast cancer since 1990. Nevertheless, misinformation about screening continues to be promoted, adding to the confusion. Despite claims to the contrary, a careful reading of the guidelines issued by major groups such as the U.S. Preventive Services Task Force and the American College of Physicians shows that they all agree that most lives are saved by screening starting at the age of 40. There is no scientific support for using the age of 50 as a threshold for screening. All women should be provided with the facts and not false information about breast cancer screening so that they can make "informed decisions" for themselves about whether to participate.


Assuntos
Neoplasias da Mama , Detecção Precoce de Câncer , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/prevenção & controle , Comunicação , Detecção Precoce de Câncer/métodos , Feminino , Humanos , Mamografia/métodos , Programas de Rastreamento/métodos
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