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1.
Medicine (Baltimore) ; 99(41): e22382, 2020 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-33031273

RESUMO

BACKGROUND: Mammography is considered a fundamental part of diagnosis in modern health care services. It provides low dose images of normal structures and pathological soft tissues in the breast. Many reports suggested that intervention is playing a positive role in anxiety related to mammography, but there is no high-quality evidence to prove its effects. This paper reports the protocol of a systematic review (SR) and meta-analysis (MA) to clarify effectiveness of intervention during screening mammography. METHODS: A systematic literature search will be performed in the Cochrane Library, PubMed, Embase and Web of Science from inception to July 2020. Randomized controlled trials (RCTs) will be included to evaluate any interventions in the treatment of anxiety related to mammography screening. The main outcome measure is the impact on patient anxiety, and the impact on patient breast cancer worry, the impact on patient satisfaction are the additional outcome measure. Risk of bias assessment of the included RCTs will be carried out using Cochrane Collaboration's tool for RCTs. The Review Manager 5.4 for Windows will be used to perform the MA and generate the result figures. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be used to evaluate the quality of evidence. Subgroup analysis and sensitivity analysis will be conducted to assess the robustness of the results. RESULTS: A total of 782 English studies of anxiety related to mammography screening were obtained through search. After preliminary screening, 773 non-conforming studies were excluded. Finally, nine English studies of anxiety related to mammography screening will be included for full-text assessment. We will submit the results of this SR and MA to a peer-reviewed journal for publication. CONCLUSIONS: This study will provide reliable evidence for intervention for reducing anxiety in women receiving screening mammography. INPLASY REGISTRATION NUMBER: INPLASY202070131.


Assuntos
Ansiedade/prevenção & controle , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/psicologia , Projetos de Pesquisa , Detecção Precoce de Câncer , Feminino , Humanos , Mamografia , Programas de Rastreamento , Metanálise como Assunto , Revisões Sistemáticas como Assunto
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1532-1535, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018283

RESUMO

18FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common location among metastatic sites, this work aims to compare different approaches to segment the bones and bone metastatic lesions in breast cancer.Two deep learning methods based on U-Net were developed and trained to segment either both bones and bone lesions or bone lesions alone on PET/CT images. These methods were cross-validated on 24 patients from the prospective EPICUREseinmeta metastatic breast cancer study and were evaluated using recall and precision to measure lesion detection, as well as the Dice score to assess bones and bone lesions segmentation accuracy.Results show that taking into account bone information in the training process allows to improve the precision of the lesions detection as well as the Dice score of the segmented lesions. Moreover, using the obtained bone and bone lesion masks, we were able to compute a PET bone index (PBI) inspired by the recognized Bone Scan Index (BSI). This automatically computed PBI globally agrees with the one calculated from ground truth delineations.Clinical relevance- We propose a completely automatic deep learning based method to detect and segment bones and bone lesions on 18FDG PET/CT in the context of metastatic breast cancer. We also introduce an automatic PET bone index which could be incorporated in the monitoring and decision process.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Fluordesoxiglucose F18 , Neoplasias da Mama/diagnóstico por imagem , Humanos , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons , Estudos Prospectivos , Tomografia Computadorizada por Raios X
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1536-1539, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018284

RESUMO

Semi-automatic measurements are performed on 18FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-automatic tools cannot make the difference between cancerous regions and active organs, presenting a high 18FDG uptake.In this work, we combine a deep learning-based approach with a superpixel segmentation method to segment the main active organs (brain, heart, bladder) from full-body PET images. In particular, we integrate a superpixel SLIC algorithm at different levels of a convolutional network. Results are compared with a deep learning segmentation network alone. The methods are cross-validated on full-body PET images of 36 patients and tested on the acquisitions of 24 patients from a different study center, in the context of the ongoing EPICUREseinmeta study. The similarity between the manually defined organ masks and the results is evaluated with the Dice score. Moreover, the amount of false positives is evaluated through the positive predictive value (PPV).According to the computed Dice scores, all approaches allow to accurately segment the target organs. However, the networks integrating superpixels are better suited to transfer knowledge across datasets acquired on multiple sites (domain adaptation) and are less likely to segment structures outside of the target organs, according to the PPV.Hence, combining deep learning with superpixels allows to segment organs presenting a high 18FDG uptake on PET images without selecting cancerous lesion, and thus improves the precision of the semi-automatic tools monitoring the evolution of breast cancer metastasis.Clinical relevance- We demonstrate the utility of combining deep learning and superpixel segmentation methods to accurately find the contours of active organs from metastatic breast cancer images, to different dataset distributions.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Algoritmos , Encéfalo , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Humanos , Metástase Neoplásica , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons
4.
Br J Radiol ; 93(1114): 20200679, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32877209

RESUMO

Italy has one of the highest COVID-19 clinical burdens in the world and Lombardy region accounts for more than half of the deaths of the country. Since COVID-19 is a novel disease, early impactful decisions are often based on experience of referral centres.We report the re-organisation which our institute (IEO, European Institute of Oncology), a cancer referral centre in Lombardy, went through to make our breast-imaging division pandemic-proof. Using personal-protective-equipment and innovative protocols, we provided essential breast-imaging procedures during COVID-19 pandemic without compromising cancer outcomes.The emergency management and infection-control-measures implemented in our division protected both the patients and the staff, making this experience useful for other radiology departments dealing with the pandemic.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Institutos de Câncer/organização & administração , Infecções por Coronavirus/epidemiologia , Controle de Infecções/métodos , Pandemias , Pneumonia Viral/epidemiologia , Serviço Hospitalar de Radiologia/organização & administração , Betacoronavirus , Institutos de Câncer/normas , Protocolos Clínicos , Infecções por Coronavirus/transmissão , Infecção Hospitalar/prevenção & controle , Transmissão de Doença Infecciosa/prevenção & controle , Feminino , Humanos , Itália/epidemiologia , Equipamento de Proteção Individual , Pneumonia Viral/transmissão , Serviço Hospitalar de Radiologia/normas
5.
Nat Commun ; 11(1): 4861, 2020 09 25.
Artigo em Inglês | MEDLINE | ID: mdl-32978398

RESUMO

Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/genética , Perfilação da Expressão Gênica/métodos , Heterogeneidade Genética , Biomarcadores Tumorais/genética , Mama , Ciclo Celular/genética , Feminino , Regulação Neoplásica da Expressão Gênica , Genômica , Humanos , Imageamento Tridimensional/métodos , Análise Multivariada , Prognóstico , Análise de Sobrevida , Transcriptoma
6.
Medicine (Baltimore) ; 99(33): e21736, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32872060

RESUMO

RATIONALE: Pilot studies have reported that patients with systemic lupus erythematosus (SLE) appear more likely to develop into neoplasia, especially lymphatic hyperplasia diseases. To our knowledge, this is the first case report of the concomitant onset of SLE and primary breast diffuse large B-cell lymphoma (PB-DLBCL). PATIENT CONCERNS: We reported an unusual case of the occurrence of primary breast diffuse large B-cell lymphoma in a 25-year-old female patient who had been diagnosed with SLE and treated with immunosuppressive drugs for about 4 years. She presented a 7-week history of a painless mass above the left breast and no history suggestive of any nipple discharge, fever, and weight loss. DIAGNOSIS: Ultrasonography of the breast showed that there was 1 mass in the left breast. After breast mass surgical resection, histopathological examinations were performed and revealed that it was primary breast diffuse large B-cell lymphoma. INTERVENTIONS: Treatment strategy with vincristine and dexamethasone was used to improve symptoms. However, the patient's renal function deteriorated and the blood potassium rose continuously and she and their family members refused the follow-up treatments. OUTCOMES: The patient died 8 months after she was discharged from the hospital. LESSONS: PB-DLBCL is a rare occurrence in SLE patients. Therefore, a careful examination is very important in SLE cohort, as activity of the disease and malignancy may mimic each other. Meanwhile, when symptoms cannot be explained or insensitive to treatment, the occurrence of malignant tumors must be highly considered.


Assuntos
Neoplasias da Mama/complicações , Mama/patologia , Falência Renal Crônica/etiologia , Lúpus Eritematoso Sistêmico/complicações , Linfoma Difuso de Grandes Células B/complicações , Adulto , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Neoplasias da Mama/terapia , Evolução Fatal , Feminino , Humanos , Linfoma Difuso de Grandes Células B/diagnóstico por imagem , Linfoma Difuso de Grandes Células B/patologia , Linfoma Difuso de Grandes Células B/terapia , Radiografia , Ultrassonografia
7.
Medicine (Baltimore) ; 99(37): e22097, 2020 Sep 11.
Artigo em Inglês | MEDLINE | ID: mdl-32925753

RESUMO

This study aimed to improve the diagnostic accuracy of breast diseases by combining breast imaging-reporting and data system (BI-RADS) with the enhancement intensity and pattern of contrast-enhanced spectral mammography (CESM) (this combination of BI-RADS and CESM was designated as BaC).BI-RADS was used to evaluate low-energy CESM images. Spearman nonparametric correlation analysis was performed to analyze the correlation between the enhancement intensity of CESM subtraction images and the pathological results. Odds ratio (OR) values were calculated to determine whether the enhancement pattern of CESM subtraction images is a risk factor for benign and malignant lesions. The diagnostic efficacies of BI-RADS, CESM, and BaC scores for benign and malignant breast diseases were analyzed using the receiver operating characteristic (ROC) curve.Lesions with a high enhancement intensity were more likely to be malignant than those with low enhancement intensity. Lesions with heterogeneous enhancement tended to be malignant, whereas those with homogeneous enhancement tended to be benign. No significant correlation was observed between ring enhancement and the benignity or malignancy of lesions. The area under the ROC curve of BaC was higher than that of BI-RADS or CESM, and the difference was statistically significant.The diagnostic efficacy of BI-RADS combined with CESM enhancement was superior to that of either method alone.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Mamografia , Intensificação de Imagem Radiográfica , Adulto , Idoso , Diagnóstico Diferencial , Feminino , Humanos , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Curva ROC , Estudos Retrospectivos , Adulto Jovem
9.
Medicine (Baltimore) ; 99(31): e21257, 2020 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-32756104

RESUMO

The aim of this study was to analyze kinetic and morphologic features using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with computer-aided diagnosis (CAD) to predict occult invasive components in cases of biopsy-proven ductal carcinoma in situ (DCIS).We enrolled 138 patients with 141 breasts who underwent preoperative breast MRI and were diagnosed with DCIS via ultrasonography (US)-guided core needle biopsy performed at our institution during January 2009 to December 2012. Their clinical, mammographic, ultrasonographic, MRI, and final histologic findings were retrospectively reviewed. Their mammographic, ultrasonographic, and MRI findings were analyzed according to the American College of Radiology Breast Imaging Reporting and Data System. CAD findings of detectability, initial (fast, medium, and slow) and delay (persistent, plateau, and washout) phase enhancement kinetic descriptor, peak enhancement percentage, and lesion size were evaluated. Continuous and categorical variables were analyzed using independent t test and χ or Fisher exact test, respectively. Independent factors for predicting the presence of invasive component were evaluated by multivariate logistic regression analysis.Final histologic findings revealed that 55 breasts (39%) had DCIS with an invasive component. MRI-detected, CAD-detected, or pathologic lesion size (P = .002, P = .001, P < .001, respectively), delay washout kinetics and detectability on CAD (P < .001 and P = .004, respectively), presence of symptoms (P = .01), presence of comedonecrosis (P < .001), nuclear grade (P = .001), abnormality on mammography (P = .02), or US (P = .03) were significantly different between pure DCIS and the DCIS with an invasive component group on univariate analysis. Of those findings, multivariate analysis revealed that delay washout on CAD (odds ratio [OR], 4.36; 95% confidence interval [CI], 1.96-9.69; P = .0003) and pathologic size (OR, 1.29; 95% CI 1.05-1.57; P = .014) were independent predictive factors for the presence of an invasive component.Delay washout kinetic features measured by CAD and pathologic tumor size are potentially useful for predicting occult invasion in cases of biopsy-proven DCIS.Breast MRI including a CAD system would be helpful for predicting invasive components in cases of biopsy-proven DCIS and for selecting patients for sentinel lymph node biopsy.


Assuntos
Neoplasias da Mama/patologia , Carcinoma Intraductal não Infiltrante/secundário , Diagnóstico por Computador , Imagem por Ressonância Magnética , Adulto , Idoso , Biópsia com Agulha de Grande Calibre , Neoplasias da Mama/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Feminino , Humanos , Pessoa de Meia-Idade , Metástase Neoplásica , Valor Preditivo dos Testes , Período Pré-Operatório , Ultrassonografia de Intervenção
10.
Zhongguo Yi Liao Qi Xie Za Zhi ; 44(4): 294-301, 2020 Apr 08.
Artigo em Chinês | MEDLINE | ID: mdl-32762200

RESUMO

OBJECTIVE: Feature extraction of breast tumors is very important in the breast tumor detection (benign and malignant) in ultrasound image. The traditional quantitative description of breast tumors has some shortcomings, such as inaccuracy. A simple and accurate feature extraction method has been studied. METHODS: In this paper, a new method of boundary feature extraction was proposed. Firstly, the shape histogram of ultrasound breast tumors was constructed. Secondly, the relevant boundary feature factors were calculated from a local point of view, including sum of maximum curvature, sum of maximum curvature and peak, sum of maximum curvature and standard deviation. Based on the boundary features, shape features and texture features, the linear support vector machine classifiers for benign and malignant breast tumor recognition was constructed. RESULTS: The accuracy of boundary features in the benign and malignant breast tumors classification was 82.69%. The accuracy of shape features was 73.08%. The accuracy of texture features was 63.46%. The classification accuracy of the three fusion features was 86.54%. CONCLUSIONS: The classification accuracy of boundary features was higher than that of texture features and shape features. The classification method based on multi-features has the highest accuracy and it describes the benign and malignant tumors from different angles. The research results have practical value.


Assuntos
Neoplasias da Mama , Máquina de Vetores de Suporte , Algoritmos , Neoplasias da Mama/diagnóstico por imagem , Humanos , Ultrassonografia
11.
PLoS One ; 15(8): e0229367, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790672

RESUMO

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Previsões/métodos , Algoritmos , Teorema de Bayes , Feminino , Humanos , Aprendizado de Máquina , Imageamento de Micro-Ondas , Modelos Teóricos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Máquina de Vetores de Suporte
12.
Nat Commun ; 11(1): 3840, 2020 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-32737293

RESUMO

Currently, human magnetic resonance (MR) examinations are becoming highly specialized with a pre-defined and often relatively small target in the body. Conventionally, clinical MR equipment is designed to be universal that compromises its efficiency for small targets. Here, we present a concept for targeted clinical magnetic resonance imaging (MRI), which can be directly integrated into the existing clinical MR systems, and demonstrate its feasibility for breast imaging. The concept comprises spatial redistribution and passive focusing of the radiofrequency magnetic flux with the aid of an artificial resonator to maximize the efficiency of a conventional MR system for the area of interest. The approach offers the prospect of a targeted MRI and brings novel opportunities for high quality specialized MR examinations within any existing MR system.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Cerâmica/efeitos da radiação , Espectroscopia Dielétrica/métodos , Imagem por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética/métodos , Adulto , Cerâmica/química , Espectroscopia Dielétrica/instrumentação , Radiação Eletromagnética , Desenho de Equipamento , Feminino , Voluntários Saudáveis , Humanos , Imagem por Ressonância Magnética/instrumentação , Imagens de Fantasmas , Razão Sinal-Ruído
13.
Adv Exp Med Biol ; 1252: 17-25, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32816258

RESUMO

All breast disorders found during pregnancy and lactation should be carefully evaluated. Most of them are benign, but it is essential to exclude pregnancy-associated breast cancer (PABC), which is too often diagnosed late. The first-line imaging technique is ultrasound (US), which must be completed by mammography if there is any clinical or US suspicious sign . In lactating patients with PABC , breast magnetic resonance imaging (MRI) can be useful for local assessment.Management depends on the precise analysis and BI-RADS classification of the lesion. During pregnancy and lactation, there is an overlap in imaging: many benign lesions can grow, infarct, become heterogeneous and thus suspicious, and on the other hand, PABC does not always present with typical malignant features. That is why biopsy must be performed if after the clinical and radiological evaluation the doubt persists, i.e. for all BI-RADS 4 and 5 lesions, and for some BI-RADS 3 lesions.


Assuntos
Doenças Mamárias/diagnóstico por imagem , Mama/diagnóstico por imagem , Lactação , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Imagem por Ressonância Magnética , Mamografia , Gravidez
14.
Lancet Oncol ; 21(9): 1165-1172, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32800099

RESUMO

BACKGROUND: The appropriate age range for breast cancer screening remains a matter of debate. We aimed to estimate the effect of mammographic screening at ages 40-48 years on breast cancer mortality. METHODS: We did a randomised, controlled trial involving 23 breast screening units across Great Britain. We randomly assigned women aged 39-41 years, using individual randomisation, stratified by general practice, in a 1:2 ratio, to yearly mammographic screening from the year of inclusion in the trial up to and including the calendar year that they reached age 48 years (intervention group), or to standard care of no screening until the invitation to their first National Health Service Breast Screening Programme (NHSBSP) screen at approximately age 50 years (control group). Women in the intervention group were recruited by postal invitation. Women in the control group were unaware of the study. The primary endpoint was mortality from breast cancers (with breast cancer coded as the underlying cause of death) diagnosed during the intervention period, before the participant's first NHSBSP screen. To study the timing of the mortality effect, we analysed the results in different follow-up periods. Women were included in the primary comparison regardless of compliance with randomisation status (intention-to-treat analysis). This Article reports on long-term follow-up analysis. The trial is registered with the ISRCTN registry, ISRCTN24647151. FINDINGS: 160 921 women were recruited between Oct 14, 1990, and Sept 24, 1997. 53 883 women (33·5%) were randomly assigned to the intervention group and 106 953 (66·5%) to the control group. Between randomisation and Feb 28, 2017, women were followed up for a median of 22·8 years (IQR 21·8-24·0). We observed a significant reduction in breast cancer mortality at 10 years of follow-up, with 83 breast cancer deaths in the intervention group versus 219 in the control group (relative rate [RR] 0·75 [95% CI 0·58-0·97]; p=0·029). No significant reduction was observed thereafter, with 126 deaths versus 255 deaths occurring after more than 10 years of follow-up (RR 0·98 [0·79-1·22]; p=0·86). INTERPRETATION: Yearly mammography before age 50 years, commencing at age 40 or 41 years, was associated with a relative reduction in breast cancer mortality, which was attenuated after 10 years, although the absolute reduction remained constant. Reducing the lower age limit for screening from 50 to 40 years could potentially reduce breast cancer mortality. FUNDING: National Institute for Health Research Health Technology Assessment programme.


Assuntos
Fatores Etários , Neoplasias da Mama/diagnóstico , Detecção Precoce de Câncer/normas , Mamografia/normas , Adulto , Idoso , Mama/diagnóstico por imagem , Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Mamoplastia , Pessoa de Meia-Idade , Sistema de Registros , Reino Unido
15.
Ann R Coll Surg Engl ; 102(8): 577-580, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32777930

RESUMO

INTRODUCTION: An increasing quantity of data is required to guide precision medicine and advance future healthcare practices, but current analytical methods often become overwhelmed. Artificial intelligence (AI) provides a promising solution. Plastic surgery is an innovative surgical specialty expected to implement AI into current and future practices. It is important for all plastic surgeons to understand how AI may affect current and future practice, and to recognise its potential limitations. METHODS: Peer-reviewed published literature and online content were comprehensively reviewed. We report current applications of AI in plastic surgery and possible future applications based on published literature and continuing scientific studies, and detail its potential limitations and ethical considerations. FINDINGS: Current machine learning models using convolutional neural networks can evaluate breast mammography and differentiate benign and malignant tumours as accurately as specialist doctors, and motion sensor surgical instruments can collate real-time data to advise intraoperative technical adjustments. Centralised big data portals are expected to collate large datasets to accelerate understanding of disease pathogeneses and best practices. Information obtained using computer vision could guide intraoperative surgical decisions in unprecedented detail and semi-autonomous surgical systems guided by AI algorithms may enable improved surgical outcomes in low- and middle-income countries. Surgeons must collaborate with computer scientists to ensure that AI algorithms inform clinically relevant health objectives and are interpretable. Ethical concerns such as systematic biases causing non-representative conclusions for under-represented patient groups, patient confidentiality and the limitations of AI based on the quality of data input suggests that AI will accompany the plastic surgeon, rather than replace them.


Assuntos
Inteligência Artificial , Interpretação de Imagem Assistida por Computador , Procedimentos Cirúrgicos Reconstrutivos , Big Data , Mama/diagnóstico por imagem , Mama/cirurgia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Feminino , Humanos , Mamografia
16.
Medicine (Baltimore) ; 99(27): e20977, 2020 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-32629712

RESUMO

BACKGROUND: Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Rapid progress in the artificial intelligence (AI) field has led to the advent of newer CAD systems using deep learning-based algorithms which have the potential to reach human performance levels. Those systems, however, have been developed using mammography images mainly from women in western countries. Because Asian women characteristically have higher-density breasts, it is uncertain whether those AI systems can apply to Japanese women. In this study, we will construct a deep learning-based CAD system trained using mammography images from a large number of Japanese women with high quality reading. METHODS: We will collect digital mammography images taken for screening or diagnostic purposes at multiple institutions in Japan. A total of 15,000 images, consisting of 5000 images with breast cancer and 10,000 images with benign lesions, will be collected. At least 1000 images of normal breasts will also be collected for use as reference data. With these data, we will construct a deep learning-based AI system to detect breast cancer on mammograms. The primary endpoint will be the sensitivity and specificity of the AI system with the test image set. DISCUSSION: When the ability of AI reading is shown to be on a par with that of human reading, images of normal breasts or benign lesions that do not have to be read by a human can be selected by AI beforehand. Our AI might work well in Asian women who have similar breast density, size, and shape to those of Japanese women. TRIAL REGISTRATION: UMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/.


Assuntos
Aprendizado Profundo , Mamografia/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Neoplasias da Mama/diagnóstico por imagem , Estudos de Casos e Controles , Feminino , Humanos , Japão , Estudos Retrospectivos
17.
Anticancer Res ; 40(7): 3915-3924, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32620632

RESUMO

BACKGROUND: Wire-guided localization (WGL) has been the mainstay for localizing non-palpable breast lesions before excision. Due to its limitations, various wireless alternatives have been developed. In this prospective study, we evaluate the role of radiation-free wireless localization using the SAVI SCOUT® localization at a European centre. PATIENTS AND METHODS: This technique was evaluated in a prospective cohort of 20 patients. The evaluation focused on clinical and pathological parameters in addition to patient and physician acceptance. RESULTS: SAVI SCOUT reflectors (n=23) were deployed to localize 22 occult breast lesions and one axillary lymph node in 20 patients. The mean deployment duration was 5.6 min, with a mean distance from the lesion of 0.6 mm. The migration rate was 0% and the mean identification and retrieval time was 25.1 min. In patients undergoing therapeutic excision for malignancy (n=17), only one (5.9%) required reoperation for positive surgical margins. Radiologists and surgeons rated the technique as better than WGL and patient satisfaction was high. CONCLUSION: Our study demonstrates that wireless localization using SAVI SCOUT® is an effective and time-efficient alternative to WGL with excellent physician and patient acceptance.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Diagnóstico por Imagem/métodos , Adulto , Idoso , Neoplasias da Mama/cirurgia , Diagnóstico por Imagem/instrumentação , Feminino , Humanos , Raios Infravermelhos , Mamografia/instrumentação , Mamografia/métodos , Pessoa de Meia-Idade , Radar
18.
Medicine (Baltimore) ; 99(28): e20847, 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32664078

RESUMO

High-grade ductal carcinoma in situ (DCIS) requires resection due to the high risk of developing invasive breast cancer. The predictive powers of noninvasive predictors for high-grade DCIS remain contradictory. This study aimed to explore the predictive value of calcification for high-grade DCIS in Chinese patients.This was a retrospective study of Chinese DCIS patients recruited from the Women's Hospital, School of Medicine, Zhejiang University between January and December 2018. The patients were divided into calcification and non-calcification groups based on the mammography results. The correlation of calcification with the pathologic stage of DCIS was evaluated using the multivariable analysis. The predictive value of calcification for DCIS grading was examined using the receiver operating characteristics (ROC) curve.The pathologic grade of DCIS was not associated with calcification morphology (P = .902), calcification distribution (P = .252), or breast density (P = .188). The multivariable analysis showed that the presence of calcification was independently associated with high pathologic grade of DCIS (OR = 3.206, 95% CI = 1.315-7.817, P = .010), whereas the age, hypertension, menopause, and mammography BI-RADS were not (all P > .05) associated with the grade of DCIS. The ROC analysis of the predictive value of calcification for DCIS grading showed that the area under the curve was 0.626 (P = .019), with a sensitivity of 73.1%, specificity of 52.2%, positive predictive value of 72.2%, and negative predictive value of 53.3%.The presence of calcification is independently associated with high pathologic grade of DCIS and could predict high-grade DCIS in Chinese patients.


Assuntos
Neoplasias da Mama/patologia , Calcinose/patologia , Carcinoma Intraductal não Infiltrante/patologia , Gradação de Tumores/métodos , Adulto , Fatores Etários , Grupo com Ancestrais do Continente Asiático/etnologia , Neoplasias da Mama/diagnóstico por imagem , Calcinose/classificação , Carcinoma Intraductal não Infiltrante/etnologia , Feminino , Humanos , Hipertensão/epidemiologia , Mamografia/métodos , Menopausa/fisiologia , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade
19.
Medicine (Baltimore) ; 99(29): e21243, 2020 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-32702902

RESUMO

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Aumento da Imagem , Aprendizado de Máquina , Imagem por Ressonância Magnética , Pessoa de Meia-Idade , Redes Neurais de Computação , Reprodutibilidade dos Testes , Estudos Retrospectivos
20.
S Afr Med J ; 110(2): 118-122, 2020 Jan 29.
Artigo em Inglês | MEDLINE | ID: mdl-32657681

RESUMO

BACKGROUND: Data on the association between HIV and breast cancer mammographic patterns and histological subtypes are limited. OBJECTIVES: To determine whether specific mammographic findings, histological features and patient profiles were unique to a cohort of HIV-positive patients who developed breast cancer, by comparing them with a HIV-negative cohort. METHODS: This was a descriptive study in which we conducted a retrospective chart review and mammographic and pathology analysis of newly diagnosed breast cancer patients referred to the Addington Hospital breast clinic between August 2008 and June 2012 and entered into a prospective database. RESULTS: Thirty-eight HIV-positive and 38 HIV-negative patients were included in the study. HIV-positive patients were more likely to have multifocal breast cancer (p=0.007), but not multicentric disease (p=0.05). The presence of grouped and fine pleomorphic microcalcifications and positive HIV status demonstrated statistical significance (p=0.000). A statistically significant relationship between grouped and fine pleomorphic microcalcifications with biopsies confirming high-grade ductal carcinoma in situ (HGDCIS) and HIV status was demonstrated (p=0.001). The mean age of the HIV-positive patients was 42.5 years (p=0.000). CONCLUSIONS: We demonstrated a statistically significant relationship between HIV status, the presence of multifocal breast cancer, and mammographically detected grouped and fine pleomorphic microcalcifications. A statistically significant relationship between HGDCIS and HIV status, and the presence of grouped and fine pleomorphic microcalcifications in HIV-positive patients with biopsies confirming HGDCIS, was demonstrated. Our study also showed that there is a relationship between age of presentation and HIV status.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Calcinose/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Infecções por HIV/epidemiologia , Mamografia/métodos , Adulto , Fatores Etários , Biópsia , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/patologia , Calcinose/patologia , Carcinoma Intraductal não Infiltrante/epidemiologia , Carcinoma Intraductal não Infiltrante/patologia , Feminino , Humanos , Pessoa de Meia-Idade , Estudos Retrospectivos
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