A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification.

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions.

Antioxidant modulation of F2-isoprostanes in humans: a systematic review.

F2-isoprostanes are a biomarker of lipid peroxidation, and their measurement has emerged as a reliable approach to assess oxidative stress. However, dietary intervention studies in humans have provided contrasting results following supplementation with antioxidant-rich foods or supplements. In this paper, we have systematically reviewed the evidence about the effect of supplementation with antioxidant-rich foods and galenic antioxidants on isoprostanes levels in humans. Moreover, the association with nonenzymatic antioxidant capacity (NEAC), a biomarker of endogenous antioxidant status, has also been investigated. MEDLINE database was searched using the terms "(isoprostane* OR isoP OR iso-PGF OR epi-PGF) AND (intervention* OR consumption* OR administration* OR supplementation*)," with limits activated "humans" and "English." Abstracts and full texts were screened, from which were selected human intervention studies reporting isoprostanes measurement in biological fluids. The total of the studies carried out with antioxidant-rich foods and antioxidant galenic supplements was 113, reporting 154 interventions. Results suggest that dietary antioxidants modulate successfully the levels of isoprostanes in less than 45% of the interventions. A correspondence between the effect on isoprostane and NEAC has been evidenced, and this correspondence suggests the importance of measuring different biomarkers to obtain a better outline of the redox events following supplementation.

Coexistence of granular cell tumor and invasive ductal breast cancer in contralateral breasts: a case report.

Granular cell tumor (GCT) is a benign tumor of the breast that can mimic, on breast imaging, invasive carcinomas. Biological evolution of mammary GCT is unknown, especially if it is associated with an invasive carcinoma in the same or contralateral breast. This report details the morphological features of these synchronous lesions highlighting their biological characteristics and suggesting an appropriate follow up.

Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography.

PURPOSE: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. METHODS: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon-Mann-Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. RESULTS: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. CONCLUSIONS: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon-Mann-Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions.

Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis.

PURPOSE: To quantitatively assess the dose of Dual energy contrast enhanced digital mammography (CEDM) and digital breast tomosynthesis (DBT) and to investigate the relationship between average absorbed glandular dose (AGD), compressed breast thickness (CBT) and compression force (CF). MATERIALS AND METHODS: All CEDM and DBT examinations were performed in cranio-caudal (CC) and medio-lateral oblique (MLO) view. Exposure parameters of 135 mammographic procedures that using AEC (automatic exposure control) mode were recorded. AGDs were calculated. Kruskal Wallis test was performed. RESULTS: CBT population ranged from 23 to 94 mm with a thickness median value of 52 mm in CC view and of 57 mm in MLO views. CEDM AGD median value was significatively lower than DBT AGD in each views (p << 0.01). AGD showed a positive correlation and linear regression with CBT for both CEDM and DBT while CF did not show a correlation and linear regression with AGD. The highest values were found for MLO view: R2 of 0.74 for CEDM and R2 of 0.61 for DBT. Kruskal Wallis test shows that there was a difference statistically significant between AGD values of CEDM and DBT in CC view respect to MLO views (p < 0.01). CONCLUSIONS: Dose values of both techniques meet the recommendations for maximum dose in mammography. The results of the present study indicated that there was significant difference between AGD for CEDM and DBT exposure in different views (AGD in CC views had the lowest value) and that CBT could influence the AGD while CF was not correlated to AGD.

Original normal-phase high-performance liquid chromatographic separation of monoacylglycerol classes from extra virgin olive oil triacylglycerols for their stereospecific analysis.

An innovative procedure to separate the 3 isomeric sn-monoacylglycerols (MAG) classes (sn-1-, sn-2-, sn-3-MAG) is described. MAGs, obtained by chemical deacylation of triacylglycerols (TAGs), have been derivatized with (S)-(+)-1-(1-naphtyl)ethyl-isocyanate, and the resulting urethane derivatives have been separated by normal-phase high-performance liquid chromatography. This procedure allows resolution as diasteroisomers of the 2 enantiomeric classes (sn-1-MAG and sn-3-MAG), without the need of a chiral column, and to separate also the isomeric sn-2-MAG class; moreover, by introducing a chromophoric moiety, this reagent makes possible the ultraviolet detection of the analyte molecules. This procedure has been used to obtain the stereospecific analysis of the TAG fraction of extra virgin olive oil samples. The use of a nondestructive detector permitted the collection of the individual urethane classes; the fatty acid composition of each was determined by high-resolution gas chromatography, obtaining directly from the data the fatty acid distribution within each sn- position of TAGs. To validate this new method, the results have been compared with those obtained by 2 other procedures for TAG stereospecific analysis, and the obtained results were satisfactory since the proposed method gave data very similar to the other procedures.

Color-doppler using contrast medium in evaluating the response to neoadjuvant treatment in patients with locally advanced breast carcinoma.

BACKGROUND: The aim of our study was to assess the color-Doppler ultrasound (CDU) pattern in the analysis of neoadjuvant preoperative treatment of patients with locally advanced breast carcinoma, improvement after injection of contrast medium (Levovist) and possible correlations between morphological and vascular aspects of the neoplasm and postoperative histopathological findings. MATERIALS AND METHODS: We studied 50 patients affected by locally advanced breast carcinoma (T3a e b-T4), using CDU before and after injection of Levovist, prior to and after neoadjuvant chemotherapeutic treatment. RESULTS: The use of Levovist for ultrasound examinations prior to treatment revealed a higher number of vascular signals in 94% of the lesions compared to the basic color-Doppler examination; in only 3 cases (6%) were no modifications observed after injection of the contrast medium. This finding was also evident after neoadjuvant treatment, as a greater number of vessels in 28 lesions were observed, in addition to residual vascularization in 9 patients in whom the basic color-Doppler examination demonstrated substantial avascularity. Histopathology revealed that this method was more sensitive in disclosing the presence of active neoplastic tissue. CONCLUSION: Color-Doppler ultrasound is the first step in assessing the efficacy of neochemotherapeutic treatment in patients affected by locally advanced breast carcinoma. Levovist increases sensitivity and improves the diagnostic precision, thus allowing for a better image of the vessels, which is an important index of the biological activity of the neoplasm, compared to the basic color-Doppler examination.

Matrix effect in F2-isoprostanes quantification by HPLC-MS/MS: a validated method for analysis of iPF2α-III and iPF2α-VI in human urine.

Liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) has become the method of choice for analysis in biological matrices, because of its high specificity and sensitivity. However, it should be taken into account that the presence of matrix components coeluting with analytes might interfere with the ionization process and affect the accuracy and precision of the assay. For this reason, the presence of a "matrix effect" should always be evaluated during method development, above all in complex matrix such as urine. In the present work, a HPLC-MS/MS method was developed for the quantification of urinary iPF2α-III and iPF2α-VI. A careful assessment of matrix effect and an accurate validation were carried out, in order to verify the reliability of quantitative data obtained. Ion suppression, due to the matrix components, was reduced through optimization of both chromatographic method and sample extraction procedure. Urine samples were purified by solid phase extraction (SPE) and the extracts injected into the HPLC-MS/MS system, equipped with a TurboIonSpray ionization source operated in negative ion mode (ESI(-)). Stable isotope-labeled analogues (iPF2α-III-d4 and iPF2α-VI-d4) were used as internal standards, and quantification was performed in multiple reaction monitoring (MRM) mode by monitoring the following mass transitions: m/z 353.4→193.2 for iPF2α-III, m/z 357.2→197.0 for iPF2α-III-d4, m/z 353.4→115.1 for iPF2α-VI, and m/z 357.4→115.1 for iPF2α-VI-d4. The validated assay, applied to the analysis of urinary samples coming from healthy and overweight subjects, resulted suitable for an accurate quantification of iPF2α-III and iPF2α-VI in human urine.