scholarly journals Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

2019 ◽  
Vol 8 (6) ◽  
pp. 891 ◽  
Author(s):  
Annarita Fanizzi ◽  
Liliana Losurdo ◽  
Teresa Maria A. Basile ◽  
Roberto Bellotti ◽  
Ubaldo Bottigli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Positive Rate ◽  
Region Of Interest ◽  
Classification Model ◽  
Support Tool ◽  
Contrast Enhanced ◽  
Positive Rate ◽  
Sequential Feature Selection ◽  
Fully Automatic ◽  
Automated Support

Contrast-Enhanced Spectral Mammography (CESM) is a novelty instrumentation for diagnosing of breast cancer, but it can still be considered operator dependent. In this paper, we proposed a fully automatic system as a diagnostic support tool for the clinicians. For each Region Of Interest (ROI), a features set was extracted from low-energy and recombined images by using different techniques. A Random Forest classifier was trained on a selected subset of significant features by a sequential feature selection algorithm. The proposed Computer-Automated Diagnosis system is tested on 48 ROIs extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. The present method resulted highly performing in the prediction of benign/malignant ROIs with median values of sensitivity and specificity of 87 . 5 % and 91 . 7 % , respectively. The performance was high compared to the state-of-the-art, even with a moderate/marked level of parenchymal background. Our classification model outperformed the human reader, by increasing the specificity over 8 % . Therefore, our system could represent a valid support tool for radiologists for interpreting CESM images, both reducing the false positive rate and limiting biopsies and surgeries.

scholarly journals Breast Cancer Preoperative Staging: Does Contrast-Enhanced Magnetic Resonance Mammography Modify Surgery?

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chiara Perono Biacchiardi ◽  
Davide Brizzi ◽  
Franco Genta ◽  
Eugenio Zanon ◽  
Marco Camanni ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance ◽  
Tumour Size ◽  
False Positive Rate ◽  
Preoperative Staging ◽  
Newly Diagnosed ◽  
Invasive Tumour ◽  
Contrast Enhanced ◽  
Magnetic Resonance Mammography ◽  
Positive Rate

Women with newly diagnosed breast cancer may have lesions undetected by conventional imaging. Recently contrast-enhanced magnetic resonance mammography (CE-MRM) showed higher sensitivity in breast lesions detection. The present analysis was aimed at evaluating the benefit of preoperative CE-MRM in the surgical planning. From 2005 to 2009, 525 consecutive women (25–75 years) with breast cancer, newly diagnosed by mammography, ultrasound, and needle-biopsy, underwent CE-MRM. The median invasive tumour size was 19 mm. In 144 patients, CE-MRM identified additional lesions. After secondlook, 119 patients underwent additional biopsy. CE-MRM altered surgery in 118 patients: 57 received double lumpectomy or wider excision (41 beneficial), 41 required mastectomy (40 beneficial), and 20 underwent contra lateral surgery (18 beneficial). The overall false-positive rate was 27.1% (39/144). CE-MRM contributed significantly to the management of breast cancer, suggesting more extensive disease in 144/525 (27.4%) patients and changing the surgical plan in 118/525 (22.5%) patients (99/525, 18.8% beneficial).

scholarly journals Handling Multiplicity in Neuroimaging through Bayesian Lenses with Multilevel Modeling

2017 ◽  
Author(s):  
Gang Chen ◽  
Yaqiong Xiao ◽  
Paul A. Taylor ◽  
Justin K. Rajendra ◽  
Tracy Riggins ◽  
...  
Keyword(s):  
Multiple Testing ◽  
False Positive Rate ◽  
Region Of Interest ◽  
Brain Regions ◽  
Integrative Model ◽  
Multidimensional Data ◽  
Positive Rate ◽  
Incorrect Sign ◽  
Sign Type ◽  
Univariate Approach

AbstractHere we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in thep-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

dtectDx Breast: A serum biomarker test for breast cancer detection used in conjunction with traditional mammography screening.

2013 ◽  
Vol 31 (26_suppl) ◽  
pp. 18-18
Author(s):  
Meredith C. Henderson ◽  
Keri Sweeten ◽  
Sherri Borman ◽  
Christa Corn ◽  
Lindsey Gordon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Positive Rate ◽  
Standard Of Care ◽  
Patient Characteristics ◽  
Protein Biomarkers ◽  
Time Period ◽  
Positive Rate ◽  
Previous Clinical Study

18 Background: Provista Diagnostics has developed a test that analyzes serum concentrations of 5 protein biomarkers in order to detect breast cancer. The dtectDx Breast test utilizes a proprietary algorithm that has been described previously (Weber et al. 2010). In this study, it was noted that the algorithm performs best in women under age 50. The aim of this study was to evaluate the performance characteristics of dtectDx Breast in women under age 50 in a commercial setting and compare the results with data from the previous clinical study. Methods: The dtectDx Breast test measures the concentrations of IL-8, IL-12, VEGF, CEA, and HGF via ELISA. These data combined with select patient characteristics and Provista’s proprietary algorithm result in a test value that is characterized as normal or elevated. dtectDx Breast test reports issued for women under age 50 were reviewed from a 3-year time period and prescribing physicians were interviewed regarding follow-up care and outcome measures (largely imaging studies, if warranted). Results: Of the 908 patients, 8 samples were rejected based on serum quality. Of the remaining 900 patients, 121 were reported as elevated (12.7%). In 4 cases, these elevated results were confirmed cases of breast cancer. Of these, 2 patients initially showed no screening evidence of cancer, but upon further evaluation (after receipt of dtectDx Breast results) were diagnosed with breast cancer. dtectDx correctly identified DCIS 66% of the time (n=2). Conclusions: These results describe the use of dtectDx Breast in a clinical setting and confirm that the assay behaves similarly to previously published results (Weber et al 2010). While the false-positive rate is higher than predicted (12.7% vs 6.8%), the assay correctly identified 4 of 4 invasive cancers and 2 of 3 DCIS cases. Since two of the invasive cancer cases were originally not detected via standard screening procedures, the assay has demonstrated important clinical utility when used in conjunction with mammography/standard of care. Here we show that, in the commercial patient population, when combined with standard of care, dtectDx Breast improves the detection of breast cancer in women under 50.

DYNAMIC MRI FOR TUMOR ENHANCEMENT: AN EXPERIMENTAL BREAST CANCER MODEL

2013 ◽  
Vol 25 (01) ◽  
pp. 1350011 ◽  
Author(s):  
Ting-Kai Leung ◽  
Pai-Jung Huang ◽  
Chi-Ming Lee ◽  
Chih-Hsiung Wu ◽  
Yi-Fan Chen ◽  
...  
Keyword(s):  
False Positive Rate ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Dynamic Mri ◽  
Future Research ◽  
Post Processing ◽  
Contrast Enhanced ◽  
Novel Drugs ◽  
Positive Rate ◽  
Magnetic Resonance Imaging Mri

Dynamic contrast-enhanced magnetic resonance imaging (MRI) with post-processing is routinely used for the analysis of tumors. However, although breast MRI has gained broad clinical recognition, the relationship between imaging findings and tumor pathogenesis has yet to be fully elucidated. We grafted tumors on rats, to examine dynamic MRI images of the tumors, using post-processing subtraction with 3D maximum intensity projection (sMIP). We established a preliminary platform for analysis to compare hemodynamic-based images with histopathological findings and to further biomolecular research. This platform could facilitate future research on the mechanisms of breast tumor enhancement using MRI, improvements to MRI analysis and reduction of the false positive rate, and the development of novel drugs and contrast media.

scholarly journals Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI

2008 ◽  
Vol 105 (46) ◽  
pp. 17937-17942 ◽  
Author(s):  
Xin Li ◽  
Wei Huang ◽  
Elizabeth A. Morris ◽  
Luminita A. Tudorica ◽  
Venkatraman E. Seshan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Malignant Tumors ◽  
Region Of Interest ◽  
Contrast Enhanced ◽  
Fast Exchange ◽  
Contrast Reagent ◽  
Cr Concentration ◽  
Tissue Compartments ◽  
Kinetics Of ◽  
Exchange Regime

The passage of a vascular-injected paramagnetic contrast reagent (CR) bolus through a region-of-interest affects tissue 1H2O relaxation and thus MR image intensity. For longitudinal relaxation [R1 ≡ (T1)−1], the CR must have transient molecular interactions with water. Because the CR and water molecules are never uniformly distributed in the histological-scale tissue compartments, the kinetics of equilibrium water compartmental interchange are competitive. In particular, the condition of the equilibrium trans cytolemmal water exchange NMR system sorties through different domains as the interstitial CR concentration, [CRo], waxes and wanes. Before CR, the system is in the fast-exchange-limit (FXL). Very soon after CRo arrival, it enters the fast-exchange-regime (FXR). Near maximal [CRo], the system could enter even the slow-exchange-regime (SXR). These conditions are defined herein, and a comprehensive description of how they affect quantitative pharmacokinetic analyses is presented. Data are analyzed from a population of 22 patients initially screened suspicious for breast cancer. After participating in our study, the subjects underwent biopsy/pathology procedures and only 7 (32%) were found to have malignancies. The transient departure from FXL to FXR (and apparently not SXR) is significant in only the malignant tumors, presumably because of angiogenic capillary leakiness. Thus, if accepted, this analysis would have prevented the 68% of the biopsies that proved benign.

scholarly journals Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

2021 ◽  
Vol 11 (23) ◽  
pp. 11398
Author(s):  
Salvador Castro-Tapia ◽  
Celina Lizeth Castañeda-Miranda ◽  
Carlos Alberto Olvera-Olvera ◽  
Héctor A. Guerrero-Osuna ◽  
José Manuel Ortiz-Rodriguez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Imaging Techniques ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Fine Tuning ◽  
Cad System ◽  
Leading Role ◽  
Operational Characteristics ◽  
Positive Rate

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

scholarly journals What are the Best Ways to Reduce the False-positive Rate of 18F-FDG PET/CT in Patients with Breast Cancer?

2011 ◽  
Vol 45 (1) ◽  
pp. 85-86
Author(s):  
Laura Evangelista ◽  
Zora Baretta ◽  
Lorenzo Vinante ◽  
Guido Sotti
Keyword(s):  
Breast Cancer ◽  
False Positive ◽  
Fdg Pet ◽  
False Positive Rate ◽  
Pet Ct ◽  
Positive Rate ◽  
18F Fdg ◽  
Fdg Pet Ct
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