RESUMO
Breast cancer usually metastasizes by haematogenous spread. This is a case report of a woman with unusual liver metastases from a recurrent invasive ductal carcinoma via a lymphatic route draining the outer part of the breast to the liver running parallelly with the right rectus abdominis muscle, depicted by preoperative sentinel node lymphoscintigraphy. Realizing this route of metastasis can impact survival, as it has a favourable prognosis compared with haematogenous metastasis, we want to draw attention to this.
Assuntos
Neoplasias da Mama , Carcinoma Ductal de Mama , Neoplasias Hepáticas , Metástase Linfática , Recidiva Local de Neoplasia , Humanos , Feminino , Neoplasias da Mama/patologia , Neoplasias Hepáticas/secundário , Neoplasias Hepáticas/diagnóstico por imagem , Carcinoma Ductal de Mama/secundário , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/diagnóstico por imagem , Recidiva Local de Neoplasia/patologia , Metástase Linfática/diagnóstico por imagem , Pessoa de Meia-IdadeRESUMO
The objective of this study was to evaluate the intramammary distribution of MRI-detected mass and focus lesions that were difficult to identify with conventional B-mode ultrasound (US) alone. Consecutive patients with lesions detected with MRI but not second-look conventional B-mode US were enrolled between May 2015 and June 2023. Following an additional supine MRI examination, we performed third-look US using real-time virtual sonography (RVS), an MRI/US image fusion technique. We divided the distribution of MRI-detected mammary gland lesions as follows: center of the mammary gland versus other (superficial fascia, deep fascia, and atrophic mammary gland). We were able to detect 27 (84%) of 32 MRI-detected lesions using third-look US with RVS. Of these 27 lesions, 5 (19%) were in the center of the mammary gland and 22 (81%) were located in other areas. We were able to biopsy all 27 lesions; 8 (30%) were malignant and 19 (70%) were benign. Histopathologically, three malignant lesions were invasive ductal carcinoma (IDC; luminal A), one was IDC (luminal B), and four were ductal carcinoma in situ (low-grade). Malignant lesions were found in all areas. During this study period, 132 MRI-detected lesions were identified and 43 (33%) were located in the center of the mammary gland and 87 (64%) were in other areas. Also, we were able to detect 105 of 137 MRI-detected lesions by second-look conventional-B mode US and 38 (36%) were located in the center of the mammary gland and 67 (64%) were in other areas. In this study, 81% of the lesions identified using third-look US with RVS and 64% lesions detected by second-look conventional-B mode US were located outside the center of the mammary gland. We consider that adequate attention should be paid to the whole mammary gland when we perform third-look US using MRI/US fusion technique.
Assuntos
Neoplasias da Mama , Imageamento por Ressonância Magnética , Ultrassonografia Mamária , Humanos , Feminino , Imageamento por Ressonância Magnética/métodos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Pessoa de Meia-Idade , Adulto , Ultrassonografia Mamária/métodos , Idoso , Imagem Multimodal/métodos , Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologiaRESUMO
OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification. METHODS: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding. RESULTS: It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC. CONCLUSIONS: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.
Assuntos
Neoplasias da Mama , Carcinoma Ductal de Mama , Aprendizado Profundo , Redes Neurais de Computação , Humanos , Feminino , Neoplasias da Mama/patologia , Neoplasias da Mama/classificação , Neoplasias da Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/classificação , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/secundário , Metástase NeoplásicaRESUMO
OBJECTIVE: The ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation. METHODS: B-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis. RESULTS: A total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44-14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07-1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03-20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49-394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41-107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614-0.820) for the training set and 0.817 (95% CI, 0.738-0.890) for the validation set. The Hosmer-Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance. CONCLUSIONS: B-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.
Assuntos
Neoplasias da Mama , Linfonodos , Metástase Linfática , Nomogramas , Humanos , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Feminino , Metástase Linfática/diagnóstico por imagem , Estudos Retrospectivos , Pessoa de Meia-Idade , Linfonodos/patologia , Linfonodos/diagnóstico por imagem , Linfonodos/cirurgia , Adulto , Prognóstico , Idoso , Fatores de Risco , Seguimentos , Ultrassonografia Mamária/métodos , Ultrassonografia/métodos , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/cirurgia , Carcinoma Ductal de Mama/secundárioRESUMO
BACKGROUND: BreastScreen Australia, the population mammographic screening program for breast cancer, uses two-view digital screening mammography ± ultrasound followed by percutaneous biopsy to detect breast cancer. Secondary breast imaging for further local staging, not performed at BreastScreen, may identify additional clinically significant breast lesions. Staging options include further mammography, bilateral ultrasound, and/or contrast-based imaging (CBI) [magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM)]. CBI for local staging of screen-detected cancer was introduced at an academic hospital breast service in Melbourne, VIC, Australia. We report findings for otherwise occult disease and resulting treatment changes. MATERIAL AND METHODS: Patients staged using CEM between November 2018 and April 2022 were identified from hospital records. Data were extracted from radiology, pathology, and breast unit databases. CEM-detected abnormalities were documented as true positive (TP) for invasive cancer or ductal carcinoma in situ (DCIS), or otherwise false positive (FP). The impact on surgical decisions was assessed. RESULTS: Of 202 patients aged 44-84 years, 60 (30%) had 74 additional findings [34 (46%) TP, 40 (54%) FP]. These were malignant in 29/202 (14%) patients (79% invasive cancers, 21% DCIS). CEM resulted in surgical changes in 43/202 (21%) patients: wider resection (24/43), conversion to mastectomy (6/43), contralateral breast surgery (6/43), additional ipsilateral excision (5/43), and bracketing (2/43). Additional findings were more common for patients with larger index lesions and for invasive cancer, but there was no significant variation by age, breast density, or index lesion grade. CONCLUSIONS: CEM for local staging of screen-detected breast cancers identified occult malignancy in 14% of patients. CEM improves local staging and may facilitate appropriate management of screen-detected breast cancers.
Assuntos
Neoplasias da Mama , Carcinoma Intraductal não Infiltrante , Meios de Contraste , Detecção Precoce de Câncer , Mamografia , Estadiamento de Neoplasias , Humanos , Feminino , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Mamografia/métodos , Pessoa de Meia-Idade , Idoso , Adulto , Idoso de 80 Anos ou mais , Detecção Precoce de Câncer/métodos , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/patologia , Carcinoma Intraductal não Infiltrante/cirurgia , Seguimentos , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/cirurgia , Imageamento por Ressonância Magnética/métodos , Prognóstico , Ultrassonografia Mamária/métodos , Estudos RetrospectivosRESUMO
OBJECTIVE: The research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative breast cancer (TNBC). MATERIAL AND METHODS: This retrospective study included consecutive patients histologically diagnosed with TNBC who underwent breast DCE-MRI in 2010-2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps of wash-in and wash-out were computed and breast lesions were manually segmented, drawing a 5 mm-Region of Interest (ROI) inside the tumor and another 5 mm-ROI inside the contralateral healthy gland. Features for each map and each ROI were extracted with Pyradiomics-3D Slicer and considered first separately (tumor and contralateral gland) and then together. In each analysis the more important features for BRCA1 status classification were selected with Maximum Relevance Minimum Redundancy algorithm and used to fit four classifiers. RESULTS: The population included 67 patients and 86 lesions (21 in BRCA1-mutated, 65 in non BRCA-carriers). The best classifiers for BRCA mutation were Support Vector Classifier and Logistic Regression in models fitted with both gland and tumor features, reaching an Area Under ROC Curve (AUC) of 0.80 (SD 0.21) and of 0.79 (SD 0.20), respectively. Three features were higher in BRCA1-mutated compared to non BRCA-mutated: Total Energy and Correlation from gray level cooccurrence matrix, both measured in contralateral gland in wash-out maps, and Root Mean Squared, selected from the wash-out map of the tumor. CONCLUSIONS: This study showed the feasibility of a radiomic study with breast DCE-MRI and the potential of radiomics in predicting BRCA1 mutational status.
Assuntos
Proteína BRCA1 , Meios de Contraste , Imageamento por Ressonância Magnética , Mutação , Neoplasias de Mama Triplo Negativas , Humanos , Feminino , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto , Neoplasias de Mama Triplo Negativas/diagnóstico por imagem , Neoplasias de Mama Triplo Negativas/genética , Neoplasias de Mama Triplo Negativas/patologia , Proteína BRCA1/genética , Idoso , Mama/diagnóstico por imagem , Curva ROC , Algoritmos , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/genética , RadiômicaRESUMO
Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.
Assuntos
Neoplasias da Mama , Carcinoma Ductal de Mama , Impedância Elétrica , Tomografia , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Tomografia/métodos , Carcinoma Ductal de Mama/diagnóstico por imagem , Distribuição Normal , Mama/diagnóstico por imagem , Simulação por Computador , Algoritmos , Processamento de Imagem Assistida por Computador/métodosRESUMO
ABSTRACT: We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.
Assuntos
Neoplasias da Mama , Mama , Carcinoma Ductal de Mama , Nomogramas , Ultrassonografia Mamária , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Diagnóstico Diferencial , Ultrassonografia Mamária/métodos , Carcinoma Ductal de Mama/diagnóstico por imagem , Adulto , Mama/diagnóstico por imagem , Cicatriz/diagnóstico por imagem , Idoso , Reprodutibilidade dos Testes , Estudos Retrospectivos , RadiômicaAssuntos
Axila , Neoplasias da Mama , Terapia Neoadjuvante , Linfonodo Sentinela , Feminino , Humanos , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/tratamento farmacológico , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/cirurgia , Metástase Linfática/diagnóstico por imagem , Linfocintigrafia , Compostos Radiofarmacêuticos , Linfonodo Sentinela/diagnóstico por imagem , Linfonodo Sentinela/patologia , Biópsia de Linfonodo SentinelaAssuntos
Neoplasias Encefálicas , Neoplasias da Mama , Carcinoma Ductal de Mama , Neoplasias Pulmonares , Feminino , Humanos , Pessoa de Meia-Idade , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/secundário , Diagnóstico Diferencial , Evolução Fatal , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/secundário , Tomografia Computadorizada por Raios X , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/secundário , BiópsiaRESUMO
Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.
Assuntos
Neoplasias da Mama , Carcinoma Ductal de Mama , Imagem de Difusão por Ressonância Magnética , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Pessoa de Meia-Idade , Estudos Prospectivos , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Imagem de Difusão por Ressonância Magnética/métodos , Sensibilidade e Especificidade , Adulto , Mama/diagnóstico por imagem , Mama/patologia , Idoso , Imageamento por Ressonância Magnética/métodosRESUMO
Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [P = .04] and 0.73 vs 0.62 for CNN-LSTM models [P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.
Assuntos
Neoplasias da Mama , Carcinoma Ductal de Mama , Carcinoma Intraductal não Infiltrante , Meios de Contraste , Aprendizado Profundo , Imageamento por Ressonância Magnética , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Neoplasias da Mama/cirurgia , Pessoa de Meia-Idade , Imageamento por Ressonância Magnética/métodos , Estudos Retrospectivos , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/patologia , Carcinoma Intraductal não Infiltrante/cirurgia , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/cirurgia , Idoso , Adulto , Valor Preditivo dos Testes , Interpretação de Imagem Assistida por Computador/métodos , Mama/diagnóstico por imagem , Mama/patologia , Mama/cirurgiaRESUMO
Objective: To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans
Assuntos
Algoritmos , Neoplasias da Mama , Imageamento por Ressonância Magnética , Receptor ErbB-2 , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Receptor ErbB-2/metabolismo , Estudos Retrospectivos , Pessoa de Meia-Idade , Adulto , Curva ROC , Carcinoma Ductal de Mama/diagnóstico por imagem , Idoso , Meios de Contraste , RadiômicaRESUMO
Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which can play a crucial role in the early detection of abnormal tissue morphology. We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as Machine learning (ML) output images were analysed effectively. The support vector machine image classification model achieved 95.78% validation accuracy and 94.81% testing accuracy with polarization parameter dataset. The study's findings demonstrate the potential of Stokes-Mueller polarimetry in tissue characterization and diagnosis, providing a valuable tool for biomedical applications.
Assuntos
Neoplasias da Mama , Aprendizado de Máquina , Microscopia de Polarização , Humanos , Microscopia de Polarização/métodos , Neoplasias da Mama/patologia , Feminino , Máquina de Vetores de Suporte , Processamento de Imagem Assistida por Computador/métodos , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/classificação , Carcinoma Ductal de Mama/diagnóstico por imagemRESUMO
INTRODUCTION: Lesions of uncertain malignant potential (B3) represent 10% of core needle biopsies (CNBs) or vacuum-assisted breast biopsies (VABBs). Traditionally, B3 lesions are operated on. This study investigated the association between B3 subtypes and malignancy to determine the best management. METHODS: Pre- and postoperative histological reports from 226 patients, who had undergone excisional surgery for B3 lesions, following CNB or VABB, were retrospectively analyzed. The correlation between the CNB/VABB diagnosis and the final pathology was investigated, along with the correlation between malignancy upgrade and the type of mammographic lesion. The positive predictive value (PPV) of malignancy of B3 lesions was calculated by simple logistic regression. Patients without cancer diagnosis underwent a 7-y follow-up. RESULTS: Pathology showed 171 (75.6%) benign and 55 (24.3%) malignant lesions. The PPV was 24.3% (P = 0.043), including 31 (13.7%) ductal carcinomas in situ and 24 (10.6%) invasive carcinomas. The most frequently upgraded lesions were atypical ductal hyperplasia, 34.2% (P = 0.004), followed by lobular intraepithelial neoplasia, 27.5% (P = 0.025). The median diameter of mammographic lesions was 1.5 [0.9-2.5] cm, while for surgical specimens, it was 5 [4-7] cm (P < 0.0001). Mammographic findings and histology showed a significant correlation (P = 0.038). After a 7-y follow-up, 15 (8.9%) patients developed carcinoma, and 7 patients (4%) developed a new B3 lesion. CONCLUSIONS: We can conclude that atypical ductal hyperplasia and lobular intraepithelial neoplasia still require surgery for a significant PPV. Other types that lacked significance or confidence intervals were too wide to draw any conclusion.
Assuntos
Neoplasias da Mama , Valor Preditivo dos Testes , Humanos , Feminino , Neoplasias da Mama/patologia , Neoplasias da Mama/cirurgia , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto , Idoso , Seguimentos , Biópsia com Agulha de Grande Calibre , Mamografia , Mama/patologia , Mama/diagnóstico por imagem , Mama/cirurgia , Carcinoma Intraductal não Infiltrante/patologia , Carcinoma Intraductal não Infiltrante/diagnóstico , Carcinoma Intraductal não Infiltrante/cirurgia , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/cirurgia , Carcinoma Ductal de Mama/diagnóstico , Carcinoma Ductal de Mama/diagnóstico por imagem , Idoso de 80 Anos ou maisRESUMO
An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.
Assuntos
Neoplasias da Mama , Aprendizado Profundo , Tomografia de Coerência Óptica , Humanos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Neoplasias da Mama/patologia , Tomografia de Coerência Óptica/métodos , Feminino , Estudos Prospectivos , Pessoa de Meia-Idade , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/cirurgia , Carcinoma Ductal de Mama/patologia , Idoso , Adulto , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/cirurgia , Carcinoma Intraductal não Infiltrante/patologia , Período IntraoperatórioRESUMO
BACKGROUND: Breast cancer remains the most commonly diagnosed cancer in women. Breast-conserving surgery (BCS) is the standard approach for small low-risk tumors. If the efficacy of cryoablation is demonstrated, it could provide a minimally invasive alternative to surgery. PURPOSE: To determine the success of ultrasound-guided cryoablation in achieving the absence of Residual Invasive Cancer (RIC) for patients with ER + /HER2- tumors ≤ 2cm and sonographically negative axillary nodes. MATERIALS AND METHODS: This prospective study was carried out from April 2021 to June 2023, and involved 60 preoperative cryoablation procedures on ultrasound-visible, node-negative (cN0) infiltrating ductal carcinomas (IDC). Standard diagnostic imaging included mammography and tomosynthesis, supplemented by ultrasound-guided biopsy. MRI was performed in patients with associated intraductal carcinoma (DCIS) and an invasive component on core needle biopsy (18 out of 22 cases). All tumors were tagged with ferromagnetic seeds. A triple-phase protocol (freezing-thawing-freezing) with Argon was used, with an average procedure duration of 40 min. A logistic regression model was applied to determine significant correlation between RIC and the study variables. RESULTS: Fifty-nine women (mean age 63 ± 8 years) with sixty low-risk unifocal IDC underwent cryoablation prior to surgery. Pathological examination of lumpectomy specimens post-cryoablation revealed RIC in only one of 38 patients with pure IDC and in 4 of 22 mixed IDC/DCIS cases. All treated tumors had clear surgical margins, with no significant procedural complications. CONCLUSIONS: Cryoablation was effective in eradicating 97% of pure infiltrating ER + /HER2-tumors ≤ 2cm, demonstrating its potential as a surgical alternative in selected patients.
Assuntos
Neoplasias da Mama , Criocirurgia , Receptor ErbB-2 , Humanos , Feminino , Criocirurgia/métodos , Neoplasias da Mama/cirurgia , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Idoso , Receptor ErbB-2/metabolismo , Estudos Prospectivos , Prognóstico , Neoplasia Residual , Adulto , Receptores de Estrogênio/metabolismo , Carcinoma Ductal de Mama/cirurgia , Carcinoma Ductal de Mama/patologia , Carcinoma Ductal de Mama/diagnóstico por imagem , Mastectomia Segmentar/métodos , Idoso de 80 Anos ou mais , Cuidados Pré-Operatórios/métodosRESUMO
BACKGROUND: Nipple sparing mastectomy (NSM) is increasingly being performed for patients with breast cancer. However, optimal postoperative surveillance has not been defined. METHODS: A prospectively maintained database identified patients with in-situ and invasive cancer who underwent NSM between 2007-2021. Clinical data on postoperative breast surveillance and interventions were collected. Patients who had MRI surveillance versus clinical breast exam (CBE) alone were compared with respect to tumor characteristics, recurrence, and survival. RESULTS: A total of 483 NSMs were performed on 399 patients. 255 (63.9%) patients had invasive ductal carcinoma, 31 (7.8%) invasive lobular carcinoma, 92 (23.1%) DCIS, 6 (1.5%) mixed ductal and lobular carcinoma, 9 (2.3%) others, and 6 (1.5%) unknown. Postoperatively, 265 (66.4%) patients were followed with CBE alone and 134 (33.6%) had surveillance MRIs. At a median follow-up of 33 months, 20 patients (5.0%) developed in-breast recurrence, 6 patients had (1.5%) an axillary recurrence, and 28 with (7.0%) distant recurrence. 14 (53.8%) LRR were detected in the CBE group and 12 (46.2%) were detected in the MRI group (P = .16). Overall survival (OS) was 99%, with no difference in OS between patients who had CBE alone versus MRI (P = .46). MRI was associated with higher biopsy rates compared to CBE alone (15.8% vs. 7.8%, P = .01). CONCLUSIONS: Compared to CBE alone, the use of screening MRI following NSM results in higher rate of biopsy and no difference in overall survival.
Assuntos
Neoplasias da Mama , Imageamento por Ressonância Magnética , Recidiva Local de Neoplasia , Mamilos , Humanos , Feminino , Neoplasias da Mama/cirurgia , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Imageamento por Ressonância Magnética/métodos , Recidiva Local de Neoplasia/diagnóstico por imagem , Recidiva Local de Neoplasia/patologia , Recidiva Local de Neoplasia/epidemiologia , Recidiva Local de Neoplasia/prevenção & controle , Adulto , Mamilos/cirurgia , Mamilos/diagnóstico por imagem , Mamilos/patologia , Idoso , Carcinoma Ductal de Mama/cirurgia , Carcinoma Ductal de Mama/diagnóstico por imagem , Carcinoma Ductal de Mama/patologia , Carcinoma Lobular/cirurgia , Carcinoma Lobular/patologia , Carcinoma Lobular/diagnóstico por imagem , Mastectomia Subcutânea/métodos , Seguimentos , Exame Físico , Estudos ProspectivosRESUMO
OBJECTIVE: The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS: Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A "radiomic model" was created with only radiomic features, and a "patho-radiomic model" was created using radiomic features and immunohistochemical data. RESULTS: Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model's sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). CONCLUSIONS: Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.