Role of quantitative pharmacokinetic parameter (transfer constant: Ktrans) in the characterization of breast lesions on MRI

Jena, Amarnath; Taneja, Sangeeta; Mehta, Shashi Bhushan

doi:10.4103/0971-3026.113614

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…In this study, a statistically significant difference was observed in K trans between the benign and malignant groups (P=0.032, 1.14 min –1 vs . 1.91 min –1 ), which was consistent with previous reports ( 21 , 22 ). However, the sensitivity and specificity were 59.6% and 66.7%, respectively, which were lower than those of previous studies.…”

Section: Discussionsupporting

confidence: 93%

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Zhao

et al. 2018

Chinese Journal of Cancer Research

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ObjectiveTo develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).MethodsEighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI.ResultsA total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency.ConclusionsADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.

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Section: Discussionsupporting

confidence: 93%

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Zhao

et al. 2018

Chinese Journal of Cancer Research

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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: 91%

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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“…Quantitative information derived from full-length DCE-MRI acquisitions (approximately 10 min in length) has demonstrated added benefit in distinguishing malignancies [ 5 , 13 , 24 ]. Specifically, the volume transfer constant, K trans , has been shown to statistically distinguish malignant from benign lesions, including in the ultra-fast DCE-MRI setting with superior temporal resolution [ 13 , 24 , 25 , 26 ]. Still, quantitative DCE-MRI can be challenging to incorporate into the clinical workflow as it requires higher temporal resolution data which comes at the expense of missing spatial resolution in the images required by radiologists.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

et al. 2021

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This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

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Role of quantitative pharmacokinetic parameter (transfer constant: Ktrans) in the characterization of breast lesions on MRI

Cited by 19 publications

References 11 publications

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

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

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

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