NESTOR DE BARROS

(Fonte: Lattes)
Índice h a partir de 2011
10
Projetos de Pesquisa
Unidades Organizacionais
Departamento de Radiologia, Faculdade de Medicina - Docente
Instituto de Radiologia, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/44 - Laboratório de Ressonância Magnética em Neurorradiologia, Hospital das Clínicas, Faculdade de Medicina

Resultados de Busca

Agora exibindo 1 - 10 de 34
  • bookPart
    Carcinoma de mama: diagnóstico
    (2016) MENDES, Daniele Carvalho Calvano; CHALA, Luciano Fernandes; BARROS, Nestor de; FILASSI, Jose Roberto
  • article 13 Citação(ões) na Scopus
    The Impact of Breast Cancer Screening Among Younger Women in the Barretos Region, Brazil
    (2013) MATTOS, Jaco Saraiva De Castro; MAUAD, Edmundo Carvalho; SYRJANEN, Kari; LONGATTO-FILHO, Adhemar; HAIKEL, Raphael Luiz; VIEIRA, Rene Aloisio Da Costa; SILVA, Thiago Buosi; BARROS, Nestor De
    Aim: To verify the impact of breast cancer screening in women aged 40-49 years in one region of Brazil. Patients and Methods: This is a cross-sectional study, targeted to asymptomatic women aged 40-69 years who had breast cancer screening mammography performed between January 2003 and December 2007. Logistic regression was used to estimate the risk of breast cancer by age groups (40-49, 5059, 60-69 years). Results: Of the 27,133 screened women, 51.9% (14,082) were aged between 40-49 years. The odds ratio (OR) of breast cancer among the 45-49 year age cohort was not significantly different from that of 60 to 69-year-old women (OR=0.64; 95% Confidence Interval 0.39 to 1.03). Conclusion: The risk of breast cancer among women aged 45 to 49 years is equivalent to that of women aged 60 to 69 years, indicating that breast cancer screening in this region of Brazil should start at the age of 45 years or immediately thereafter.
  • article 4 Citação(ões) na Scopus
    Radiological findings of breast involvement in benign and malignant systemic diseases
    (2018) MATSUMOTO, Renato Augusto Eidy Kiota; CATANI, Juliana Hiraoka; CAMPOY, Mirela Liberato; OLIVEIRA, Arthur Magalhães; BARROS, Nestor de
    Abstract Although the primary purpose of periodic mammograms in screening programs is to identify lesions suspected of being carcinomas, the findings are often related to systemic (benign or malignant) diseases, rather than breast cancer. Although the involvement of breast structures in systemic diseases is unusual, it can be included in the differential diagnosis of masses, skin changes, calcifications, asymmetry, and axillary lymphadenopathy. The main diagnostic entities that can be associated with such involvement are diabetes, chronic kidney disease, heart diseases, connective tissue diseases, HIV infection, lymphoma, leukemia, and metastases from primary tumors at other sites. In many cases, information related to knowledge and treatment of chronic diseases is not available to the radiologist at the time of evaluation of the mammography findings. The purpose of this essay is to offer relevant pictorial information to the general radiologist about systemic diseases involving the breast, expanding the range of differential diagnoses in order to avoid unnecessary invasive procedures.
  • conferenceObject
    Brazilian randomized study: Impact of preoperative magnetic resonance in the evaluation for breast cancer conservative surgery (BREAST-MRI Trial)
    (2019) MOTA, B. S.; REIS, Y. N.; DORIA, M. T.; RICCI, M. D.; SHIMIZU, C.; FERREIRA, V.; TUCUNDUVA, T.; BARROS, N. de; BARACAT, E. C.; FILASSI, J. R.
  • conferenceObject
    Exploratory Learning with Convolutional Autoencoder for Discrimination of Architectural Distortion in Digital Mammography
    (2019) OLIVEIRA, Helder C. R. de; MELO, Carlos F. E.; CATANI, Juliana H.; BARROS, Nestor de; VIEIRA, Marcelo A. da Costa
    This work presents a deep learning approach based on autoencoder to improve the detection of architectural distortion (AD) in digital mammography. AD can be the earliest sign of breast cancer, appearing before the formation of any mass or calcification. However, it is very difficult to be detected and almost 50% of the cases are missed by the radiologists. Thus, we designed an autoencoder, based on a convolutional neural network (CNN), to work as a feature descriptor in a computer-aided detection (CAD) pipeline with the objective of detecting AD in digital mammography. This model was trained with 140,000 regions-of-interest (ROI) extracted from clinical mammograms. These samples were divided in two groups, with and without AD, according to the radiologist's report. Validation was done comparing the classifier performance when using the proposed autoencoder and other well-known feature descriptors, commonly used for the task of detecting AD in digital mammograms. The results showed that the performance of the autoencoder is slightly higher than that of other descriptors. However, the complexity and the computational cost of the autoencoder is much higher when compared to the hand-crafted descriptors.
  • conferenceObject
    Brazilian Randomized Study - BREAST-MRI Trial - Impact of Preoperative Magnetic Resonance in the Evaluation for Breast Cancer Conservative Surgery: Local recurrence and surgical outcomes
    (2023) MOTA, Bruna S.; REIS, Yedda N.; BARROS, Nestor; CARDOSO, Natalia; MOTA, Rosa S.; SHIMIZU, Carlos; TUCUNDUVA, Tatiana; GONCALVES, Rodrigo; DORIA, Maira T.; FERREIRA, Vera; RICCI, Marcos; TRINCONI, Angela; RIERA, Rachel; BARACAT, Edmund C.; SOARES JR., Jose Maria; FILASSI, Jose Roberto
  • bookPart
    Aplicação do ACR BI-RADS® nos métodos de imagem em mama
    (2017) BARROS, Nestor de; DEBS, Cecília Lemos; CASTRO, Flávio Spinola; COSTENARO, Marco; JACINTO, Bruna Maria Trompson; TUCUNDUVA, Tatiana
  • conferenceObject
    Brazilian randomized study - Impact of preoperative magnetic resonance in the evaluation for breast cancer conservative surgery (BREAST-MRI trial)
    (2018) DORIA, Maira T.; MOTA, Bruna S.; REIS, Yedda N.; RICCI, Marcos D.; PIATO, Jose R. M.; FERREIRA, Vera C. C. S.; SHIMIZU, Carlos; BARROS, Nestor; FILASSI, Jose R.; BARACAT, Edmund C.
  • article 8 Citação(ões) na Scopus
    Suspicious amorphous microcalcifications detected on full-field digital mammography: correlation with histopathology
    (2018) FERREIRA, Vera Christina Camargo de Siqueira; ETCHEBEHERE, Elba Cristina Sá de Camargo; BEVILACQUA, José Luiz Barbosa; BARROS, Nestor de
    Abstract Objective: To evaluate suspicious amorphous calcifications diagnosed on full-field digital mammography (FFDM) and establish correlations with histopathology findings. Materials and Methods: This was a retrospective study of 78 suspicious amorphous calcifications (all classified as BI-RADS® 4) detected on FFDM. Vacuum-assisted breast biopsy (VABB) was performed. The histopathological classification of VABB core samples was as follows: pB2 (benign); pB3 (uncertain malignant potential); pB4 (suspicion of malignancy); and pB5 (malignant). Treatment was recommended for pB5 lesions. To rule out malignancy, surgical excision was recommended for pB3 and pB4 lesions. Patients not submitted to surgery were followed for at least 6 months. Results: Among the 78 amorphous calcifications evaluated, the histopathological analysis indicated that 8 (10.3%) were malignant/suspicious (6 classified as pB5 and 2 classified as pB4) and 36 (46.2%) were benign (classified as pB2). The remaining 34 lesions (43.6%) were classified as pB3: 33.3% were precursor lesions (atypical ductal hyperplasia, lobular neoplasia, or flat epithelial atypia) and 10.3% were high-risk lesions. For the pB3 lesions, the underestimation rate was zero. Conclusion: The diagnosis of precursor lesions (excluding atypical ductal hyperplasia, which can be pB4 depending on the severity and extent of the lesion) should not necessarily be considered indicative of underestimation of malignancy. Suspicious amorphous calcifications correlated more often with precursor lesions than with malignant lesions, at a ratio of 3:1.
  • conferenceObject
    Detection of Architectural Distortion with Deep Convolutional Neural Network and Data Augmentation of Limited Dataset
    (2019) COSTA, Arthur C.; OLIVEIRA, Helder C. R.; CATANI, Juliana H.; BARROS, Nestor de; MELO, Carlos F. E.; VIEIRA, Marcelo A. C.
    Early detection of breast cancer can increase treatment efficiency. One of the earliest signs of breast cancer is the Architectural Distortion (AD), which is a subtle contraction of the breast tissue, most of the time unnoticeable. A lot of techniques have been proposed over the years to aid the detection of AD in digital mammography but only a few using a deep learning approach. One of the most successful algorithms of deep neural architecture are the Convolutional Neural Networks (CNNs). However, to assure better CNN performance, the training step requires a large volume of data. This paper presents a deep CNN architecture designed for the automatic detection of AD in digital mammography images. For the training step, we considered the data augmentation approach, to overcome the limitation of clinical dataset. CNN performance was evaluated in terms of Receiver Operating Characteristic (ROC). The measured area under the ROC curve (AUC) was 0:87 for the proposed CNN in the task of AD detection in digital mammography.