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Multimodality Breast Imaging 2021

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 19098

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Prof. Dr. Katja Pinker-Domenig

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Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th St., New York, NY 10065, USA
Interests: breast cancer; breast imaging; women's health; PET/MRI; MRI; DWI; hybrid imaging; radiomics; radiogenomics
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Dear Colleagues,

Breast cancer is the most common cancer in women and the 2nd leading cause of female cancer deaths and thus remains a major medical and socioeconomic burden. Medical imaging has always been an integral part in breast cancer care, ranging from diagnosis and staging to therapy monitoring and post-therapeutic follow-up. Imaging modalities for diagnosis and staging of breast cancer comprise mammography, digital breast tomosynthesis, ultrasound, contrast-enhanced mammography, magnetic resonance imaging (MRI), nuclear medicine imaging, as well as hybrid techniques such as PET/CT and PET/MRI. Radiomics/-genomics image analyis and artificial intelligence may aid in better differentiating between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening.

The Special Issue of Diagnostics with a focus on “Multimodality Breast Imaging” invites submission of the recent advances, current possibilities, and emerging techniques in breast imaging.

Prof. Dr. Katja Pinker-Domenig
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Breast cancer
Mammography
Ultrasound
Contrast-enhanced mammography
Digital breast tomosynthesis
MRI PET/MRI Radiomics/-genomics
Artificial Intelligence

Related Special Issue

Multimodality Breast Imaging in Diagnostics (11 articles)

Published Papers (7 papers)

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Research

18 pages, 7425 KiB

Open AccessArticle

Evaluation of a Generative Adversarial Network to Improve Image Quality and Reduce Radiation-Dose during Digital Breast Tomosynthesis

by Tsutomu Gomi, Yukie Kijima, Takayuki Kobayashi and Yukio Koibuchi

Diagnostics 2022, 12(2), 495; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12020495 - 14 Feb 2022

Cited by 5 | Viewed by 2356

Abstract

In this study, we evaluated the improvement of image quality in digital breast tomosynthesis under low-radiation dose conditions of pre-reconstruction processing using conditional generative adversarial networks [cGAN (pix2pix)]. Pix2pix pre-reconstruction processing with filtered back projection (FBP) was compared with and without multiscale bilateral filtering (MSBF) during pre-reconstruction processing. Noise reduction and preserve contrast rates were compared using full width at half-maximum (FWHM), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) in the in-focus plane using a BR3D phantom at various radiation doses [reference-dose (automatic exposure control reference dose: AECrd), 50% and 75% reduction of AECrd] and phantom thicknesses (40 mm, 50 mm, and 60 mm). The overall performance of pix2pix pre-reconstruction processing was effective in terms of FWHM, PSNR, and SSIM. At ~50% radiation-dose reduction, FWHM yielded good results independently of the microcalcification size used in the BR3D phantom, and good noise reduction and preserved contrast. PSNR results showed that pix2pix pre-reconstruction processing represented the minimum in the error with reference FBP images at an approximately 50% reduction in radiation-dose. SSIM analysis indicated that pix2pix pre-reconstruction processing yielded superior similarity when compared with and without MSBF pre-reconstruction processing at ~50% radiation-dose reduction, with features most similar to the reference FBP images. Thus, pix2pix pre-reconstruction processing is promising for reducing noise with preserve contrast and radiation-dose reduction in clinical practice. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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11 pages, 1478 KiB

Open AccessArticle

Breast Radiation Exposure of 3D Digital Breast Tomosynthesis Compared to Full-Field Digital Mammography in a Clinical Follow-Up Setting

by Marcel Opitz, Sebastian Zensen, Katharina Breuckmann, Denise Bos, Michael Forsting, Oliver Hoffmann, Martin Stuschke, Axel Wetter and Nika Guberina

Diagnostics 2022, 12(2), 456; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12020456 - 10 Feb 2022

Cited by 2 | Viewed by 2280

Abstract

According to a position paper of the European Commission Initiative on Breast Cancer (ECIBC), DBT is close to being introduced in European breast cancer screening programmes. Our study aimed to examine radiation dose delivered by digital breast tomosynthesis (DBT) and digital mammography (FFDM) in comparison to sole FFDM in a clinical follow-up setting and in an identical patient cohort. Retrospectively, 768 breast examinations of 96 patients were included. Patients received both DBT and FFDM between May 2015 and July 2019: (I) FFDM in cranio-caudal (CC) and DBT in mediolateral oblique (MLO) view, as well as a (II) follow-up examination with FFDM in CC and MLO view. The mean glandular dose (MGD) was determined by the mammography system according to Dance’s model. The MGD (standard deviation (SD), interquartile range (IQR)) was distributed as follows: (I) (CC_FFDM+MLO_DBT) (a) left FFDM_CC 1.40 mGy (0.36 mGy, 1.13–1.59 mGy), left DBT_MLO 1.62 mGy (0.51 mGy, 1.27–1.82 mGy); (b) right FFDM_CC 1.36 mGy (0.34 mGy, 1.14–1.51 mGy), right DBT_MLO 1.59 mGy (0.52 mGy, 1.27–1.62 mGy). (II) (CC_FFDM+MLO_FFDM) (a) left FFDM_CC 1.35 mGy (0.35 mGy, 1.10–1.60 mGy), left FFDM_MLO 1.40 mGy (0.39 mGy, 1.12–1.59 mGy), (b) right FFDM_CC 1.35 mGy (0.33 mGy, 1.12–1.48 mGy), right FFDM_MLO 1.40 mGy (0.36 mGy, 1.14–1.58 mGy). MGD was significantly higher for DBT mlo views compared to FFDM (p < 0.001). Radiation dose was significantly higher for DBT in MLO views compared to FFDM. However, the MGD of DBT MLO lies below the national diagnostic reference level of 2 mGy for an FFDM view. Hence, our results support the use of either DBT or FFDM as suggested in the ECIBC’s Guidelines. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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11 pages, 1206 KiB

Open AccessArticle

Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

by Shuyi Peng, Leqing Chen, Juan Tao, Jie Liu, Wenying Zhu, Huan Liu and Fan Yang

Diagnostics 2021, 11(11), 2086; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11112086 - 11 Nov 2021

Cited by 10 | Viewed by 2213

Abstract

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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12 pages, 1280 KiB

Open AccessArticle

Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics—A Pilot Reader Study

by Ioana Boca (Bene), Anca Ileana Ciurea, Cristiana Augusta Ciortea, Paul Andrei Ștefan, Lorena Alexandra Lisencu and Sorin Marian Dudea

Diagnostics 2021, 11(7), 1248; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11071248 - 13 Jul 2021

Cited by 10 | Viewed by 1953

Abstract

Background: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). Methods: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019–December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. Conclusion: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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13 pages, 1366 KiB

Open AccessArticle

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

by Isaac Daimiel Naranjo, Peter Gibbs, Jeffrey S. Reiner, Roberto Lo Gullo, Caleb Sooknanan, Sunitha B. Thakur, Maxine S. Jochelson, Varadan Sevilimedu, Elizabeth A. Morris, Pascal A. T. Baltzer, Thomas H. Helbich and Katja Pinker

Diagnostics 2021, 11(6), 919; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11060919 - 21 May 2021

Cited by 30 | Viewed by 4056

Abstract

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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Graphical abstract

12 pages, 4004 KiB

Open AccessArticle

Detection of Microcalcifications in Spiral Breast Computed Tomography with Photon-Counting Detector Is Feasible: A Specimen Study

by Matthias Wetzl, Evelyn Wenkel, Eva Balbach, Ebba Dethlefsen, Arndt Hartmann, Julius Emons, Christiane Kuhl, Matthias W. Beckmann, Michael Uder and Sabine Ohlmeyer

Diagnostics 2021, 11(5), 848; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11050848 - 09 May 2021

Cited by 7 | Viewed by 2530

Abstract

The primary objective of the study was to compare a spiral breast computed tomography system (SBCT) to digital breast tomosynthesis (DBT) for the detection of microcalcifications (MCs) in breast specimens. The secondary objective was to compare various reconstruction modes in SBCT. In total, 54 breast biopsy specimens were examined with mammography as a standard reference, with DBT, and with a dedicated SBCT containing a photon-counting detector. Three different reconstruction modes were applied for SBCT datasets (Recon1 = voxel size (0.15 mm)³, smooth kernel; Recon2 = voxel size (0.05 mm)³, smooth kernel; Recon3 = voxel size (0.05 mm)³, sharp kernel). Sensitivity and specificity of DBT and SBCT for the detection of suspicious MCs were analyzed, and the McNemar test was used for comparisons. Diagnostic confidence of the two readers (Likert Scale 1 = not confident; 5 = completely confident) was analyzed with ANOVA. Regarding detection of MCs, reader 1 had a higher sensitivity for DBT (94.3%) and Recon2 (94.9%) compared to Recon1 (88.5%; p < 0.05), while sensitivity for Recon3 was 92.4%. Respectively, reader 2 had a higher sensitivity for DBT (93.0%), Recon2 (92.4%), and Recon3 (93.0%) compared to Recon1 (86.0%; p < 0.05). Specificities ranged from 84.7–94.9% for both readers (p > 0.05). The diagnostic confidence of reader 1 was better with SBCT than with DBT (DBT 4.48 ± 0.88, Recon1 4.77 ± 0.66, Recon2 4.89 ± 0.44, and Recon3 4.75 ± 0.72; DBT vs. Recon1/2/3: p < 0.05), while reader 2 found no differences. Sensitivity and specificity for the detection of MCs in breast specimens is equal for DBT and SBCT when a small voxel size of (0.05 mm)³ is used with an equal or better diagnostic confidence for SBCT compared to DBT. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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12 pages, 5982 KiB

Open AccessArticle

Microwave Breast Imaging Using Compressed Sensing Approach of Iteratively Corrected Delay Multiply and Sum Beamforming

by Mohammad Tariqul Islam, Md Tarikul Islam, Md Samsuzzaman, Salehin Kibria and Muhammad E. H. Chowdhury

Diagnostics 2021, 11(3), 470; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11030470 - 08 Mar 2021

Cited by 14 | Viewed by 2353

Abstract

Microwave imaging (MI) is a consistent health monitoring technique that can play a vital role in diagnosing anomalies in the breast. The reliability of biomedical imaging diagnosis is substantially dependent on the imaging algorithm. Widely used delay and sum (DAS)-based diagnosis algorithms suffer from some significant drawbacks. The delay multiply and sum (DMAS) is an improved method and has benefits over DAS in terms of greater contrast and better resolution. However, the main drawback of DMAS is its excessive computational complexity. This paper presents a compressed sensing (CS) approach of iteratively corrected DMAS (CS-ICDMAS) beamforming that reduces the channel calculation and computation time while maintaining image quality. The array setup for acquiring data comprised 16 Vivaldi antennas with a bandwidth of 2.70–11.20 GHz. The power of all the channels was calculated and low power channels were eliminated based on the compression factor. The algorithm involves data-independent techniques that eliminate multiple reflections. This can generate results similar to the uncompressed variants in a significantly lower time which is essential for real-time applications. This paper also investigates the experimental data that prove the enhanced performance of the algorithm. Full article

(This article belongs to the Special Issue Multimodality Breast Imaging 2021)

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