Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging

Cheng, Zhigang; Wu, Zhuo; Shi, Guangzi; Yi, Zhilong; Xie, Mingwei; Zeng, Weike; Song, Chao; Zheng, Chushan; Shen, Jun

doi:10.1007/s00330-017-5050-2

Cited by 25 publications

(17 citation statements)

References 42 publications

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“…At a lower field strength, one has to compromise on using either a high temporal resolution suitable for PK analysis or a high spatial resolution for detailed morphologic assessment. Our findings are in good agreement with prior studies at 1.5 and 3T, which demonstrated the potential of PK parameters to aid in the differentiation of benign from malignant tumors [14,18,32,[44][45][46][47]. We integrated the information from quantitative PK analysis to established reporting guidelines, i.e., BI-RADS, showing that PK-enhanced BI-RADS improved diagnostic accuracy from 88.4% to 97.7% by reducing false-positive findings.…”

Section: Discussionsupporting

confidence: 88%

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Ochoa‐Albiztegui

Sevilimedu

Horvat

et al. 2020

Cancers

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The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.

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Section: Discussionsupporting

confidence: 88%

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Ochoa‐Albiztegui

Sevilimedu

Horvat

et al. 2020

Cancers

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“…Finally, the MRI protocol itself can also have influence on our results 55 . We aimed to control for this bias and adopted our imaging protocol to international standards and used well established criteria to assess the tumor vascularization 16,18,28,29,45,56 . Future studies should investigate the repeatability of our results with different software, MRI protocols and identify the potential impact of the reader on our results.…”

Section: Discussionmentioning

confidence: 99%

Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

Dietzel

Schulz‐Wendtland

Ellmann

et al. 2020

Sci Rep

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to investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRi before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/ DSD). the nottingham prognostic index (npi) was used as the reference method with which to predict survival of breast cancer. Based on the MRi scans, VAV was accomplished by commercially available, fDA-cleared software. DSD served as endpoint. integration of VAV into the npi gave npi VAV. prediction of DSD by npi VAV compared to standard npi alone was investigated (cox regression, likelihood-test, predictive accuracy: Harrell's C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by npi (Harrell's c = 75.3%) was enhanced by VAV (NPI VAV : Harrell's c = 81.0%). Most of all, the NPI VAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRi as a "gate keeper" in the management of breast cancer patients. Such a "gate keeper" could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted. Breast cancer is the most common malignant neoplasm of women in the western world. Despite substantial advances in diagnosis and treatment, morbidity and mortality of breast cancer remain high 1. By adjusting the treatment to the individual cancer biology, precision medicine aims to both improve disease survival and to reduce the rate of unnecessary cytotoxic treatment 2,3. To achieve this goal, accurate biomarkers are essential tools for precision medicine. They help us to understand tumor biology and allow estimation of patient outcome 4. The Nottingham Prognostic Index (NPI) is a well-established and widely used method for the prediction of survival of primary breast cancer. It is based on three classic biomarkers of breast cancer: tumor size, tumor grade, and the number of metastatic loco-regional lymph nodes 5-8. Breast MRI (MRI) is a functional imaging method that has been validated in clinical practice for over 30 years 9. Objective semi-quantitative analysis of MRI by automated, FDA-cleared software has been intensely

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“…When employed in the very early phase (0-60 s after contrast injection), it can generate kinetic parameters reflecting contrast agent inflow effects. The diagnostic utility of these generated parameters in differentiating malignant from benign lesions and in improving positive predictive value has been proven in recent years [5][6][7][8][9][10][11][12][13][14][15]. Moreover, several studies have shown that these parameters present higher or comparable accuracy to Breast Imaging Reporting and Data System (BI-RADS) [1] delayed phase kinetic curve assessment, suggesting that ultrafast DCE-MRI in the very early phase may substitute for the standard DCE-MRI delayed phase [5,7,9,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer

et al. 2020

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Background: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. Results: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001).Conclusions: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

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Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging

Cited by 25 publications

References 42 publications

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer

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