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Cancers
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MRI in Breast Cancer
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MRI in Breast Cancer

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 9562

Special Issue Editor

Prof. Patrice G. Taourel

E-Mail Website
Guest Editor
Université de Montpellier, Montpellier, France
Interests: digital breast tomosynthesis; MRI in breast cancer; CT of acute abdomen

Special Issue Information

Dear Colleagues,

Breast cancer is one of the leading malignancies for new diagnoses as well as for cancer-related mortality. Over the past decade, the diagnosis and management of breast cancer has undergone marked changes, with a growing role of diagnostic and interventional MRI.

When it comes to MRI screening, familial risk factors, personal history of breast cancer, genomic testing, and imaging factors such as the beast density at mammography and background parenchymal enhancement at MRI are useful tools to better stratify risk prognostication and potentially individualize imaging screening strategies, including MRI. The role of abbreviated and ultrafast breast MRI in this indication must be assessed more extensively.

For the characterization of breast tumor and axillary lymph node, MRI radiomics, notably including shape features, kinetic intensity-histograms, and texture matrix-based features, bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor in contrast to tissue biopsy, which samples only a small part of a potentially heterogeneous tumor. However, biopsy under MRI guidance remains the reference exam when suspicious lesions are visible on MRI only, and we need more experience focused on the way of improving the diagnostic management of enhancement detected by MR. 

For the locoregional management of breast cancer, MRI allows mapping the extent of the disease to guide surgery, and to guide management of axillary lymph nodes. However, the impact of breast MRI on the rate of multistage surgery and on the rate of recurrence is still discussed.

In the follow-up of patients with breast cancer, MRI permits to monitor the response of breast cancer after neo-adjuvant chemotherapy. Its usefulness for the assessment of the intermediate response and its impact on the surgical gesture according to the delayed response remains a topic of interest.

Lastly, the role of artificial Intelligence in detecting breast lesions on MRI and in the task of differentiating benign and malignant enhancing lesions detected at breast MRI is a fast-developing topic and will have a considerable impact on breast imaging.

For this Special Issue of Cancers, we welcome articles of different types—mini-reviews, full reviews, and data articles to address these different issues with the aim of better defining the place of MRI in breast cancer.

Prof. Patrice G. Taourel
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 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. Cancers 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 2900 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
  • screening
  • staging
  • follow-up
  • MRI

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Published Papers (2 papers)

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Research

15 pages, 1308 KiB  
Article
Prediction of Primary Tumour and Axillary Lymph Node Response to Neoadjuvant Chemo(Targeted) Therapy with Dedicated Breast [18F]FDG PET/MRI in Breast Cancer
by Cornelis M. de Mooij, Thiemo J. A. van Nijnatten, Briete Goorts, Loes F. S. Kooreman, Isabel W. M. Raymakers, Silke P. L. van Meijl, Maaike de Boer, Kristien B. M. I. Keymeulen, Joachim E. Wildberger, Felix M. Mottaghy, Marc B. I. Lobbes and Marjolein L. Smidt
Cancers 2023, 15(2), 401; https://doi.org/10.3390/cancers15020401 - 7 Jan 2023
Cited by 7 | Viewed by 3142
Abstract
Background: The aim of this study was to investigate whether sequential hybrid [18F]FDG PET/MRI can predict the final pathologic response to neoadjuvant chemo(targeted) therapy (NCT) in breast cancer. Methods: Sequential [18F]FDG PET/MRI was performed before, halfway through and after NCT, followed by surgery. [...] Read more.
Background: The aim of this study was to investigate whether sequential hybrid [18F]FDG PET/MRI can predict the final pathologic response to neoadjuvant chemo(targeted) therapy (NCT) in breast cancer. Methods: Sequential [18F]FDG PET/MRI was performed before, halfway through and after NCT, followed by surgery. Qualitative response evaluation was assessed after NCT. Quantitatively, the SUVmax obtained by [18F]FDG PET and signal enhancement ratio (SER) obtained by MRI were determined sequentially on the primary tumour. For the response of axillary lymph node metastases (ALNMs), SUVmax was determined sequentially on the most [18F]FDG-avid ALN. ROC curves were generated to determine the optimal cut-off values for the absolute and percentage change in quantitative variables in predicting response. Diagnostic performance in predicting primary tumour response was assessed with AUC. Similar analyses were performed in clinically node-positive (cN+) patients for ALNM response. Results: Forty-one breast cancer patients with forty-two primary tumours and twenty-six cases of pathologically proven cN+ disease were prospectively included. Pathologic complete response (pCR) of the primary tumour occurred in 16 patients and pCR of the ALNMs in 14 cN+ patients. The AUC of the qualitative evaluation after NCT was 0.71 for primary tumours and 0.54 for ALNM responses. For primary tumour response, combining the percentage decrease in SUVmax and SER halfway through NCT achieved an AUC of 0.78. The AUC for ALNM response prediction increased to 0.92 by combining the absolute and the percentage decrease in SUVmax halfway through NCT. Conclusions: Qualitative PET/MRI after NCT can predict the final pathologic primary tumour response, but not the ALNM response. Combining quantitative variables halfway through NCT can improve the diagnostic accuracy for final pathologic ALNM response prediction. Full article
(This article belongs to the Special Issue MRI in Breast Cancer)
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15 pages, 2520 KiB  
Article
Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI, and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience
by Roberta Fusco, Vincenza Granata, Mauro Mattace Raso, Paolo Vallone, Alessandro Pasquale De Rosa, Claudio Siani, Maurizio Di Bonito, Antonella Petrillo and Mario Sansone
Cancers 2021, 13(10), 2421; https://doi.org/10.3390/cancers13102421 - 17 May 2021
Cited by 10 | Viewed by 5284
Abstract
Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included [...] Read more.
Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including Ktrans, kep, ve, and vp were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R2*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered. Results. R2* and D had a significant negative correlation (−0.57). The mean value, standard deviation, Skewness and Kurtosis values of R2* did not show a statistical significance between benign and malignant lesions (p > 0.05) confirmed by the ‘poor’ diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of D, Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (p-value = 0.02). Significant results for the mean value of Ktrans, mean value, standard deviation value and Skewness of kep, mean value, Skewness and Kurtosis of ve were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of kep and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered. Conclusions. Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R2* and D. Full article
(This article belongs to the Special Issue MRI in Breast Cancer)
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