The expression of FHIT, PCNA and EGFR in benign and malignant breast lesions.
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ABSTRACT:
SUBMITTER: Terry G
PROVIDER: S-EPMC2360209 | biostudies-other | 2007 Jan
REPOSITORIES: biostudies-other
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Project description:BACKGROUND:Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls. METHODS:Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions. RESULTS:Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification. CONCLUSION:A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.
| S-EPMC7272048 | biostudies-literature
Project description:BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.
| S-EPMC8558475 | biostudies-literature
Project description:Elastography is a promising way to assess tissue differences regarding stiffness or elasticity, which has been historically assessed manually by palpation. Combined with conventional imaging modalities, shear wave elastography can potentially evaluate the stiffness of a breast lesion and consequently help detect malignant breast tumor from benign ones. The aim of this study was to evaluate the diagnostic role of shear wave elastography in breast lesions in the Indian population.Fifty patients presenting with breast lesions were included in the study. All the patients were subjected to B-mode ultrasound and elastography using shear wave with Virtual Touch Imaging (VTITM) (Siemens Medical Solutions USA, Inc., PA, USA) and Virtual Touch Quantification (VTQTM) (Siemens Medical Solutions USA, Inc., PA, USA) and the obtained data was analyzed using an appropriate statistical test (independent samples t-test).In our study group of 50 patients, 34 were benign and 16 were malignant. VTITM showed a sensitivity of 97% and a specificity of 93% with a positive predictive value (PPV) of 97% for benign lesions. VTITM showed a sensitivity of 87.5 % and a specificity of 100% with a PPV of 100% for malignant lesions. VTQTM showed a sensitivity of 71.4% and a specificity of 100% with a PPV of 100% for benign lesions. VTQTM showed a sensitivity of 100% and a specificity of 100% with a PPV of 76.6% for malignant lesions.VTITM was more reliable as a diagnostic tool compared to VTQTM in benign lesions and both are equally reliable in identifying malignant lesions. Acoustic radiation force impulse (ARFI) plays a significant role as an adjuvant diagnostic tool to B-mode imaging for assessing breast lesions.
| S-EPMC5493461 | biostudies-other
Project description:Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.
| S-EPMC7064857 | biostudies-literature
Project description:PurposeTo evaluate the kinetic patterns of benign and malignant breast lesions using contrast-enhanced digital mammogram (CEDM).MethodsWomen with suspicious breast lesions on mammography or ultrasound were enrolled. Single-view mediolateral oblique (MLO) CEDM of an affected breast was acquired at 2, 3, 4, 7, and 10 min after injection of contrast agent. Three readers visually and semi-quantitatively analyzed the enhancement of suspicious lesions. The kinetic pattern of each lesion was classified as persistent, plateau, or washout over two time intervals, 2-4 min and 2-10 min, by comparing the signal intensity at the first time interval with that at the second.ResultsThere were 73 malignant and 75 benign lesions in 148 patients (mean age: 52 years). Benign and malignant breast lesions showed the highest signal intensity at 3 min and 2 min, respectively. Average areas under receiver operating characteristic (ROC) curve for diagnostic accuracy based on lesion enhancement at different time points were 0.73 at 2 min, 0.72 at 3 min, 0.69 at 4 min, 0.67 at 7 min, and 0.64 at 10 min. Diagnostic performance was significantly better at 2, 3, and 4 min than at 7 and 10 min (all p < 0.05). A washout kinetic pattern was significantly associated with malignant lesions at 2-4 min and 2-10 min frames according to two of the three readers' interpretations (all p ≤ 0.001).ConclusionApplications of optimal time intervals and kinetic patterns show promise in differentiation of benign and malignant breast lesions on CEDM.
| S-EPMC7498093 | biostudies-literature
Project description:There are increasing demands for informative cancer biomarkers, accessible via minimally invasive procedures, both for initial diagnostics and to follow-up personalized cancer therapy. Fine-needle aspiration (FNA) biopsy provides ready access to relevant tissues; however, the minute sample amounts require sensitive multiplex molecular analysis to achieve clinical utility. We have applied proximity extension assays (PEA) and NanoString (NS) technology for analyses of proteins and of RNA, respectively, in FNA samples. Using samples from patients with breast cancer (BC, n = 25) or benign lesions (n = 33), we demonstrate that these FNA-based molecular analyses (a) can offer high sensitivity and reproducibility, (b) may provide correct diagnosis in shorter time and at a lower cost than current practice, (c) correlate with results from routine analysis (i.e., benchmarking against immunohistochemistry tests for ER, PR, HER2, and Ki67), and (d) may also help identify new markers related to immunotherapy. A specific 11-protein signature, including FGF binding protein 1, decorin, and furin, distinguished all cancer patient samples from all benign lesions in our main cohort and in smaller replication cohort. Due to the minimally traumatic sampling and rich molecular information, this combined proteomics and transcriptomic methodology is promising for diagnostics and evaluation of treatment efficacy in BC.
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Project description:Benign breast diseases constitute a heterogeneous group of lesions arising in the mammary epithelium or in other mammary tissues, and they may also be linked to vascular, inflammatory or traumatic pathologies. Most lesions found in women consulting a physician are benign. Ultrasound (US) diagnostic criteria indicating a benign lesion are described as well as US findings in the most frequent benign breast lesions.
| S-EPMC3558101 | biostudies-literature
Project description:Heat shock protein 27 (hsp-27) is a regulator of oestrogen receptor (ER) expression and a modulator of intracellular homeostasis. In this laboratory, Shaaban et al demonstrated the importance of ER-alpha, together with Ki67, in enhancing the progression of benign breast lesions of defined morphological types. To better understand the mechanisms by which ER-alpha promotes breast neoplasia, this study was performed to test the hypothesis that the roles of ER-alpha and hsp-27 may be defined by their quantitative expression in proliferative breast lesions of varying histological risk. The expression of hsp-27 was identified using a specific monoclonal antibody and analysed to assess the proportion of positive epithelial cells using digitised morphometric image analysis. The expression of ER-alpha was analysed by immunohistochemistry and Western blotting in a variety of benign (HUMA121) and malignant mammary cell lines, including ER-alpha(+) (MCF7, ZR-75, T47D) and ER-alpha(-) (MDA-MB 231) breast cancer cell lines. The data confirm that, during progression from normal through proliferative breast lesions to in situ cancer, there was a significant increase in both the proportion and the optical density of the epithelial cells expressing hsp-27. The mean levels of expression ranged from 7.4% of the total number of epithelial cells in normal lobules to 25.17% of epithelial cells in hyperplasias of usual type (HUT) to 61.1% of epithelial cells in ductal carcinoma in situ (P<0.001). The study has confirmed the expression of hsp-27 to be closely associated with ER-alpha(+) expression, and that its regulated expression occurs early along the mammary oncogenic pathway, supporting the initial hypothesis. It is our proposal that the differential expression of hsp-27 modulates the phenotypic behaviour of morphologically benign epithelial cells and hence may be an important determinant in initiating, or promoting, a population of human mammary cancers.
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Project description:Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0-89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8-90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.
| S-EPMC10011394 | biostudies-literature
Project description:PurposeTo assess the accuracy of the shutter-speed approach compared with standard approach dynamic contrast material-enhanced magnetic resonance (MR) imaging pharmacokinetic analysis for breast cancer diagnosis.Materials and methodsThis study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained from 89 high-risk women (age range, 28-83 years) who had 92 suspicious lesions with negative findings at mammography (but visible at MR imaging). Each underwent a research dynamic contrast-enhanced MR imaging examination just prior to a clinical MR imaging-guided interventional procedure. Tumor region of interest (ROI) averaged and (for some) pixel-by-pixel dynamic contrast-enhanced time-course data, together with mean arterial input function, were subjected to serial standard and shutter-speed approach analyses to extract pharmacokinetic parameters, including rate constant for passive contrast reagent transfer between plasma and interstitium (K(trans)) and interstitial space volume fraction, or v(e). Pathologic findings were used as reference standards. Diagnostic accuracy was assessed with receiver operating characteristic analyses.ResultsThe pathologic analyses revealed 20 malignant and 72 benign lesions. Positive predictive value of the institutional clinical breast MR imaging protocol was 22%. At 100% sensitivity, ROI-averaged shutter-speed approach K(trans) had significantly (P = .008) higher diagnostic specificity than standard approach K(trans): 86.1% versus 77.8%. The difference in the ROI-averaged K(trans) parameter value, or ΔK(trans) (≡ K(trans) [shutter-speed approach] - K(trans) [standard approach]), had even higher specificity (88.9%). Combined use of ROI analysis and pixel-by-pixel mapping of ΔK(trans) achieved 98.6% specificity at 100% sensitivity.ConclusionThe use of the shutter-speed dynamic contrast-enhanced MR imaging method has the potential to improve breast cancer diagnostic accuracy and reduce putatively unnecessary biopsy procedures that yield benign pathologic findings.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11102413/-/DC1.
| S-EPMC3198224 | biostudies-literature