Multiparametric MRI Features of Breast Cancer Molecular Subtypes

Szep, Madalina; Pintican, Roxana; Boca, Bianca; Perja, Andra; Duma, Magdalena; Feier, Diana; Fetică, Bogdan; Eniu, Dan Tudor; Dudea, Sorin M; Chiorean, Angelica

doi:10.3390/medicina58121716

Cited by 11 publications

(13 citation statements)

References 36 publications

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“…One study where tumors were segmented on DWI and segmentation ROIs were propagated onto ADC maps reported high accuracy in predicting hormonal status and some molecular types such as luminal B [ 21 , 22 ]. In addition, Leithner et al [ 21 ] reported better results when first-order histogram parameters were obtained from ADC maps.…”

Section: Discussionmentioning

confidence: 99%

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

et al. 2023

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There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.

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Section: Discussionmentioning

confidence: 99%

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

et al. 2023

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“…26 Although such immunohistochemical subtypes are determined by histopathological sampling, efforts have been made to predict different subtypes on imaging, often using a multiparametric approach. 10,[27][28][29][30] Angiogenesis and metabolism are the 2 hallmarks of cancer that can be evaluated by contrast-enhanced MRI and 18 F-FDG PET. 31 Proliferation can be assessed by DWI, 20 and the increase in metabolism that precedes proliferation can be used as an early marker of tumor growth.…”

Section: Subtype Angiogenesis Metabolism and Tumor Microenvironment R...mentioning

confidence: 99%

“…However, multiparametric data are now also used to tackle more complex tasks such as predicting subtypes and treatment response after neoadjuvant treatment. We have summarized selected recent research investigating the prediction of subtypes 10,27,29,30,91,[103][104][105][106][107][108][109] (Table 2), response after neoadjuvant treatment [110][111][112] (Table 3), and evaluating TILs or CAF [113][114][115][116] (Table 4) to provide an overview of the trends in parameters and methods used for analysis. The most popular combinations of imaging include DCE, DWI (ADC value), and T2WI.…”

Section: Multiparametric Approaches To Breast Cancer: Recent Advancesmentioning

confidence: 99%

“…91 On the other hand, multivariate analysis might result in 1 independent parameter. 29 When look into detail, the performance of the CNN model may differ among subtypes. 105 Mutiparametric approach succeeded in predicting triple negative subtype.…”

Section: Multiparametric Approaches To Breast Cancer: Recent Advancesmentioning

confidence: 99%

“…105 Mutiparametric approach succeeded in predicting triple negative subtype. 10,29,30,91,105,106 Multiparametric MRI is applied to predict response after neoadjuvant treatment. Multiple parameters are used to achieve best MRI, magnetic resonance imaging; TME, tumor microenvironment; DCE, dynamic contrast-enhanced; UF, ultrafast DCE; ADC, apparent diffusion coefficient; PET, positron emission tomography; AUC, area under the receiver operating characteristic curve; TN, triple negative; HER2, human epidermal growth factor 2; NAC, neoadjuvant chemotherapy; TIL, tumor-infiltrating lymphocyte; FDG, fluorodeoxyglucose; CAF, cancer-associated fibroblast.…”

Section: Multiparametric Approaches To Breast Cancer: Recent Advancesmentioning

confidence: 99%

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Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

Kataoka,

Iima,

Miyake

et al. 2023

Invest Radiol

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A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

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