Pharmacokinetic Mapping for Lesion Classification in Dynamic Breast MRI
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Purpose: To prospectively investigate whether a rapid dynamic MRI protocol, in conjunction with pharmacokinetic modeling, could provide diagnostically useful information for discriminating biopsy-proven benign lesions from malignancies.
Materials And Methods: Patients referred to breast biopsy based on suspicious screening findings were eligible. After anatomic imaging, patients were scanned using a dynamic protocol with complete bilateral breast coverage. Maps of pharmacokinetic parameters representing transfer constant (K(trans)), efflux rate constant (k(ep)), blood plasma volume fraction (v(p)), and extracellular extravascular volume fraction (v(e)) were averaged over lesions and used, with biopsy results, to generate receiver operating characteristic curves for linear classifiers using one, two, or three parameters.
Results: Biopsy and imaging results were obtained from 93 lesions in 74 of 78 study patients. Classification based on K(trans) and k(ep) gave the greatest accuracy, with an area under the receiver operating characteristic curve of 0.915, sensitivity of 91%, and specificity of 85%, compared with values of 88% and 68%, respectively, obtained in a recent study of clinical breast MRI in a similar patient population.
Conclusion: Pharmacokinetic classification of breast lesions is practical on modern MRI hardware and provides significant accuracy for identification of malignancies. Sensitivity of a two-parameter linear classifier is comparable to that reported in a recent multicenter study of clinical breast MRI, while specificity is significantly higher.
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Jorgensen C, Linville R, Galea I, Lambden E, Vogele M, Chen C J Chem Inf Model. 2025; 65(3):1067-1084.
PMID: 39823383 PMC: 11815851. DOI: 10.1021/acs.jcim.4c01815.
Wang P, Velikina J, Bancroft L, Samsonov A, Kelcz F, Strigel R Tomography. 2022; 8(3):1552-1569.
PMID: 35736876 PMC: 9227412. DOI: 10.3390/tomography8030128.
Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W Eur Radiol. 2021; 32(3):1634-1643.
PMID: 34505195 DOI: 10.1007/s00330-021-08244-7.
Fusco R, Granata V, Mattace Raso M, Vallone P, De Rosa A, Siani C Cancers (Basel). 2021; 13(10).
PMID: 34067721 PMC: 8155852. DOI: 10.3390/cancers13102421.
Li K, Machireddy A, Tudorica A, Moloney B, Oh K, Jafarian N Tomography. 2020; 6(2):148-159.
PMID: 32548291 PMC: 7289240. DOI: 10.18383/j.tom.2019.00028.