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Open AccessEditorial

Special Issue “Advancement in Breast Diagnostic and Interventional Radiology”

by Graziella Di Grezia

Diagnostics 2022, 12(1), 217; https://doi.org/10.3390/diagnostics12010217 - 17 Jan 2022

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Abstract

A multimodality approach in breast imaging is a unique solution to guarantee to the patient a complete diagnosis [...] Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

Research

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11 pages, 3320 KiB

Open AccessArticle

Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study

by Georgios S. Ioannidis, Michalis Goumenakis, Ioannis Stefanis, Apostolos Karantanas and Kostas Marias

Diagnostics 2022, 12(2), 425; https://doi.org/10.3390/diagnostics12020425 - 6 Feb 2022

Cited by 5 | Viewed by 1867

Abstract

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as [...] Read more.

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R² metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R² of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, G_mean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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11 pages, 1975 KiB

Open AccessArticle

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI

by Silvia Tsvetkova, Katya Doykova, Anna Vasilska, Katya Sapunarova, Daniel Doykov, Vladimir Andonov and Petar Uchikov

Diagnostics 2022, 12(2), 332; https://doi.org/10.3390/diagnostics12020332 - 27 Jan 2022

Cited by 4 | Viewed by 2036

Abstract

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, [...] Read more.

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10⁻³ mm²/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10⁻³ mm²/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10⁻³ mm²/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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12 pages, 4894 KiB

Open AccessArticle

High-Resolution DWI with Simultaneous Multi-Slice Readout-Segmented Echo Planar Imaging for the Evaluation of Malignant and Benign Breast Lesions

by Shuyi Peng, Yihao Guo, Xiaoyong Zhang, Juan Tao, Jie Liu, Wenying Zhu, Leqing Chen and Fan Yang

Diagnostics 2021, 11(12), 2273; https://doi.org/10.3390/diagnostics11122273 - 4 Dec 2021

Cited by 10 | Viewed by 2707

Abstract

To investigate the feasibility and effectiveness of high-resolution readout-segmented echo planar imaging (rs-EPI), diffusion-weighted imaging (DWI) is used simultaneously with multi-slice (SMS) imaging (SMS rs-EPI) for the differentiation of breast malignant and benign lesions in comparison to conventional rs-EPI on a 3T MR [...] Read more.

To investigate the feasibility and effectiveness of high-resolution readout-segmented echo planar imaging (rs-EPI), diffusion-weighted imaging (DWI) is used simultaneously with multi-slice (SMS) imaging (SMS rs-EPI) for the differentiation of breast malignant and benign lesions in comparison to conventional rs-EPI on a 3T MR scanner. A total of 102 patients with 113 breast lesions underwent bilateral breast MRI using a prototype SMS rs-EPI sequence and a conventional rs-EPI sequence. Subjective image quality was assessed using a 5-point Likert scale (1 = poor, 5 = excellent). Signal-to-noise ratio (SNR), lesion contrast-to-noise ratio (CNR) and apparent diffusion coefficients (ADC) value of the lesion were measured for comparison. Receiver operating characteristic curve analysis was performed to evaluate the diagnosis performance of ADC, and the corresponding area under curve (AUC) was calculated. The image quality scores in anatomic distortion, lesion conspicuity, sharpness of anatomical details and overall image quality of SMS rs-EPI were significantly higher than those of conventional rs-EPI. CNR was enhanced in the high-resolution SMS rs-EPI acquisition (6.48 ± 1.71 vs. 4.23 ± 1.49; p < 0.001). The mean ADC value was comparable in SMS rs-EPI and conventional rs-EPI (benign 1.45 × 10⁻³ vs. 1.43 × 10⁻³ mm²/s, p = 0.702; malignant 0.91 × 10⁻³ vs. 0.89 × 10⁻³ mm²/s, p = 0.076). The AUC was 0.957 in SMS rs-EPI and 0.983 in conventional rs-EPI. SMS rs-EPI technique allows for higher spatial resolution and slight reduction of scan time in comparison to conventional rs-EPI, which has potential for better differentiation between malignant and benign lesions of the breast. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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10 pages, 1889 KiB

Open AccessArticle

Evaluation of Breast Galactography Using Digital Breast Tomosynthesis: A Clinical Exploratory Study

by Juan Tao, Hao Liao, Yuan Liu, Qingsong Peng, Wenying Zhu, Shuyi Peng, Jie Liu, Leqing Chen and Fan Yang

Diagnostics 2021, 11(11), 2060; https://doi.org/10.3390/diagnostics11112060 - 7 Nov 2021

Cited by 3 | Viewed by 1848

Abstract

Objectives: To compare the application value of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in breast galactography. Materials and Methods: A total of 128 patients with pathological nipple discharge (PND) were selected to undergo galactography. DBT and FFDM were performed for [...] Read more.

Objectives: To compare the application value of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in breast galactography. Materials and Methods: A total of 128 patients with pathological nipple discharge (PND) were selected to undergo galactography. DBT and FFDM were performed for each patient after injecting the contrast agent; the radiation dose of DBT and FFDM was calculated, and the image quality was evaluated in consensus by two senior breast radiologists. Histopathologic data were found in 49 of the 128 patients. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for both FFDM- and DBT-galactography were calculated using histopathologic results as a reference standard. Data were presented as percentages along with their 95% confidence intervals (CI). Results: The average age of the 128 patients was 46.53 years. The average glandular dose (AGD) of DBT-galactography was slightly higher than that of FFDM-galactography (p < 0.001). DBT-galactography was 30.7% higher than FFDM-galactography in CC view, while DBT-galactography increased by 21.7% compared with FFDM-galactography in ML view. Regarding catheter anatomic distortion, structure detail, and overall image quality groups, DBT scores were higher than FFDM scores, and the differences were significant for all measures (p < 0.05). In 49 patients with pathological nipple discharge, we found that the DBT-galactography had higher sensitivity, specificity, PPV, and NPV (93.3%, 75%, 97.7%, and 50%, respectively) than FFDM-galactography (91.1%, 50%, 95.3%, and 33.3%, respectively). Conclusions: Compared to FFDM-galactography, within the acceptable radiation dose range, DBT-galactography increases the sensitivity and specificity of lesion detection by improving the image quality, providing more confidence for the diagnosis of clinical ductal lesions. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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10 pages, 1148 KiB

Open AccessArticle

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

by Soumya Prakash Rana, Maitreyee Dey, Riccardo Loretoni, Michele Duranti, Lorenzo Sani, Alessandro Vispa, Mohammad Ghavami, Sandra Dudley and Gianluigi Tiberi

Diagnostics 2021, 11(10), 1930; https://doi.org/10.3390/diagnostics11101930 - 18 Oct 2021

Cited by 12 | Viewed by 2348

Abstract

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1–9 GHz has been developed. Machine learning (ML) has been implemented to understand information [...] Read more.

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1–9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist’s conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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14 pages, 2337 KiB

Open AccessArticle

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

by Chunli Li and Jiandong Yin

Diagnostics 2021, 11(8), 1491; https://doi.org/10.3390/diagnostics11081491 - 18 Aug 2021

Cited by 12 | Viewed by 2728

Abstract

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 [...] Read more.

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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15 pages, 1511 KiB

Open AccessArticle

Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images

by Raffaella Massafra, Samantha Bove, Vito Lorusso, Albino Biafora, Maria Colomba Comes, Vittorio Didonna, Sergio Diotaiuti, Annarita Fanizzi, Annalisa Nardone, Angelo Nolasco, Cosmo Maurizio Ressa, Pasquale Tamborra, Antonella Terenzio and Daniele La Forgia

Diagnostics 2021, 11(4), 684; https://doi.org/10.3390/diagnostics11040684 - 10 Apr 2021

Cited by 38 | Viewed by 5444

Abstract

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a [...] Read more.

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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16 pages, 112630 KiB

Open AccessCase Report

Male Breast Cancer Review. A Rare Case of Pure DCIS: Imaging Protocol, Radiomics and Management

by Daniele Ugo Tari, Luigi Morelli, Antonella Guida and Fabio Pinto

Diagnostics 2021, 11(12), 2199; https://doi.org/10.3390/diagnostics11122199 - 25 Nov 2021

Cited by 11 | Viewed by 5183

Abstract

Ductal carcinoma in situ (DCIS) of male breast is a rare lesion, often associated with invasive carcinoma. When the in situ component is present in pure form, histological grade is usually low or intermediate. Imaging is difficult as gynaecomastia is often present and [...] Read more.

Ductal carcinoma in situ (DCIS) of male breast is a rare lesion, often associated with invasive carcinoma. When the in situ component is present in pure form, histological grade is usually low or intermediate. Imaging is difficult as gynaecomastia is often present and can mask underlying findings. We report a rare case of pure high-grade DCIS in a young male patient, with associated intraductal papilloma and atypical ductal hyperplasia. Digital breast tomosynthesis (DBT) showed an area of architectural distortion at the union of outer quadrants of the left breast without gynaecomastia. Triple assessment suggested performing a nipple-sparing mastectomy, which revealed the presence of a focal area of high-grade DCIS of 2 mm. DCIS, even of high grade, is difficult to detect with mammography and even more rare, especially when associated with other proliferative lesions. DBT with 2D synthetic reconstruction is useful as the imaging step of a triple assessment and it should be performed in both symptomatic and asymptomatic high-risk men to differentiate between malignant and benign lesions. We propose a diagnostic model to early detect breast cancer in men, optimizing resources according to efficiency, effectiveness and economy, and look forward to radiomics as a powerful tool to help radiologists. Full article

(This article belongs to the Special Issue Advancement in Breast Diagnostic and Interventional Radiology)

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