A Comprehensive Evaluation of Quantitative Diffusion Parameters for Differentiating Histopathological Features and Subtypes of Breast Cancers: Diffusion Kurtosis Imaging (DKI), Intravoxel Incoherent Motion (IVIM) and Histogram Analysis of ADC

Autor: Behnam Amini, Moein Ghasemi, Fatemeh Rashidi, Dorreh Farazandeh, Niloofar Jafarimehrabady, Maryam Alaei, Mona Sedaghat, Seyyed Mohammad Mehdi Hosseini, Sarah Torabi, Nastaran Karimi, Amirhossein Parsaei, Ali Zare Dehnavi, Masih Rikhtehgar, Amir Pasha Amel Shahbaz, Maryam Vajihinejad
Rok vydání: 2023
Popis: Background The objective of this study is to quantitatively compare the diagnostic value of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) in differentiating the histopathological features and subtypes of breast cancer. Materials and Methods There were 98 patients with breast cancer studied by multiple b value DWIs and DKIs grouped according to their molecular prognostic factors. Entropy and histogram derived parameters of volumetric ADC values, true diffusivity (Dt), pseudo-diffusion coefficient (Dp), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) maps were calculated using voxel based analysis for the whole lesion volume. The diagnostic efficacy of various diffusion parameters for predicting both molecular prognostic factors (Hormone-Receptor (HR, ER or PR positive), HER2 and ki67) and breast cancer subtypes were compared. Diagnostic performance was evaluated using the univariate and multivariate logistic regressions, ROC analysis, multivariate backward logistic regression, analysis of covariance (ANCOVA) and partial eta squared (ηp2) estimation. Results HR- positive tumors had significantly lower median ADC values (P= < 0.001, Bonferroni adjusted significance < 0.002) than HR- negative tumors. HER-2 positive tumors had significantly higher mean ADC values and last ADC quartile (P< 0.001, univariate regression: OR=99.3, 14.2, AUC=0.79, 0.73, Pp2=0.86, P< 0.001) after adjusting for molecular prognostic factors. Conclusion The use of diffusion imaging with multiple b values will be beneficial for the classification of breast cancers.
Databáze: OpenAIRE